Image Processing Projects For Final Year - Advanced Computer Vision
Image processing serves as a foundational pillar in modern computational research, focusing on the systematic enhancement and analysis of visual data through mathematical algorithms. The image processing projects for final year domain covers critical areas such as feature extraction, object recognition, and medical visualization, aligning with IEEE journal standards to address complex real-world challenges in digital interpretation.
Wisen proposed systems are designed to bridge the gap between theoretical frameworks and engineering-grade implementations by integrating state-of-the-art computer vision models. By following IEEE 2025–2026 research trends, we provide a structured environment for developing robust image processing projects that demonstrate strong technical rigor and experimental validation.
Image Processing Projects - 2025-2026 Titles


Enhancing Kidney Tumor Segmentation in MRI Using Multi-Modal Medical Images With Transformers

Explainable AI for Brain Tumor Classification Using Cross-Gated Multi-Path Attention Fusion and Gate-Consistency Loss

Centralized Position Embeddings for Vision Transformers

Speckle Noise Reduction in SAR Images Using Rank Residual Constraint Regularization

HATNet: Hierarchical Attention Transformer With RS-CLIP Patch Tokens for Remote Sensing Image Captioning

Remote Sensing Image Object Detection Algorithm Based on DETR


A Tile Surface Defect Detection Algorithm Based on Improved YOLO11

Automated Classification of User Exercise Poses in Virtual Reality Using Machine Learning-Based Human Pose Estimation

2.5D-UNet-HC: 2.5D-UNet Based on Hybrid Convolution for Prostate Ultrasound Image Segmentation

MMIDNet: A Multilevel Mutual Information Disentanglement Network for Cross-Domain Infrared Small Target Detection

RESRTDETR: Cross-Scale Feature Enhancement Based on Reparameterized Convolution and Channel Modulation

Automatic Explainable Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography

Transformer-Based DME Classification Using Retinal OCT Images Without Data Augmentation: An Evaluation of ViT-B16 and ViT-B32 With Optimizer Impact

GA-UNet: Genetic Algorithm-Optimized Lightweight U-Net Architecture for Multi-Sequence Brain Tumor MRI Segmentation

RFTransUNet: Res-Feature Cross Vision Transformer-Based UNet for Building Extraction From High-Resolution Remote Sensing Images

Autonomous Road Defects Segmentation Using Transformer-Based Deep Learning Models With Custom Dataset

SD-DETR: Space Debris Detection Transformer Based on Dynamic Convolutional Network and Cross-Scale Collaborative Attention

DSCP-UNet: A Tunnel Crack Segmentation Algorithm Based on Lightweight Diminutive Size and Colossal Perception


Spatial–Temporal Feature Interaction and Multiscale Frequency-Domain Fusion Network for Remote Sensing Change Detection

Boosting the Performance of Image Restoration Models Through Training With Deep-Feature Auxiliary Guidance

CSD: Channel Selection Dropout for Regularization of Convolutional Neural Networks

Encouraging Discriminative Attention Through Contrastive Explainability Learning for Lung Cancer Diagnosis

ESRVA: Enhanced Super-Resolution and Visual Annotation Model for Object-Level Image Interpretation Using Deep Learning

BWFNet: Bitemporal Wavelet Frequency Network for Change Detection in High-Resolution Remote Sensing Images

Low-Similarity Client Sampling for Decentralized Federated Learning

Back and Forward Incremental Learning Through Knowledge Distillation for Object Detection Unmanned Aerial Vehicles

STMTNet: Spatio-Temporal Multiscale Triad Network for Cropland Change Detection in Remote Sensing Images

Innovative Methodology for Determining Basic Wood Density Using Multispectral Images and MAPIR RGNIR Camera

Copper and Aluminum Scrap Detection Model Based on Improved YOLOv11n

LSODNet: A Lightweight and Efficient Detector for Small Object Detection in Remote Sensing Images

Anomaly Detection and Segmentation in Carotid Ultrasound Images Using Hybrid Stable AnoGAN

Analyzing Lane Visibility Distance on Urban and Suburban Roads in Slovakia Under Various Weather Conditions Using a Single Camera

Published on: Sept 2025
Enhanced Lesion Localization and Classification in Ocular Tumor Detection Using Grad-CAM and Transfer Learning


Retinal Fusion Network with Contrastive Learning for Imbalanced Multi-Class Retinal Disease Recognition in FFA

Enhancing Coffee Leaf Disease Classification via Active Learning and Diverse Sample Selection

Multimodal SAM-Adapter for Semantic Segmentation

A Fine-Grained Remote Sensing Classification Approach for Mine Development Land Types Based on the Integration of HRNet and DeepLabV3+


Improving Medical X-Ray Imaging Diagnosis With Attention Mechanisms and Robust Transfer Learning Techniques

Multiscale Feature Enhancement for Water Body Segmentation in High-Resolution Remote Sensing Images

Remote Sensing Image Dehazing Using Content-Driven State Space Modeling With Scale-Aware Aggregation

Prompt-Driven Multitask Learning With Task Tokens for ORSI Salient Object Detection

GF-ResFormer: A Hybrid Gabor-Fourier ResNet-Transformer Network for Precise Semantic Segmentation of High-Resolution Remote Sensing Imagery

HMSA-Net: A Hierarchical Multi-Scale Attention Network for Brain Tumor Segmentation From Multi-Modal MRI

Detection to Framework for Traffic Signs Using a Hybrid Approach

High-Accuracy Mapping of Coastal and Wetland Areas Using Multisensor Data Fusion and Deep Feature Learning

TANet: A Multi-Representational Attention Approach for Change Detection in Very High-Resolution Remote Sensing Imagery

Adaptive Fusion of LiDAR and Camera Data for Enhanced Precision in 3D Object Detection for Autonomous Driving

Gradient-Aware Directional Convolution With Kolmogorov Arnold Network-Enhanced Feature Fusion for Road Extraction

Global Saturation-Value Translation Approach for Haze Removal in Urban Aerial Imagery

Supervised Spatially Spectrally Coherent Local Linear Embedding—Wasserstein Graph Convolutional Network for Hyperspectral Image Classification

Enhancing Worker Safety at Heights: A Deep Learning Model for Detecting Helmets and Harnesses Using DETR Architecture

A Classifier Adaptation and Adversarial Learning Joint Framework for Cross-Scene Coastal Wetland Mapping on Hyperspectral Imagery

RCM: A Novel Fire Detection Technique That Effectively Resists Interference in Complex Scenarios

An Improved Method for Zero-Shot Semantic Segmentation

Design of a CNN–Swin Transformer Model for Alzheimer’s Disease Prediction Using MRI Images

PPDM-YOLO: A Lightweight Algorithm for SAR Ship Image Target Detection in Complex Environments

YOLOv8n-GSE: Efficient Steel Surface Defect Detection Method

Mitigating the Bias in the Model for Continual Test-Time Adaptation

The Spatio-Temporal Weighted Adjustment Network for Remote Sensing Image Change Detection

Efficient Object Detection in Large-Scale Remote Sensing Images via Situation-Aware Model

Enhancing Industrial PCB and PCBA Defect Detection: An Efficient and Accurate SEConv-YOLO Approach

JDAWSL: Joint Domain Adaptation With Weight Self-Learning for Hyperspectral Few-Shot Classification

FreqSpaceNet: Integrating Frequency and Spatial Domains for Remote Sensing Image Segmentation


WU-Net: An Automatic and Lightweight Deep Learning Method for Water Body Extraction of Multispectral Remote Sensing Images

ICDRF: Indian Coin Denomination Recognition Framework

Two-Stage Neural Network Pipeline for Kidney and Tumor Segmentation

A 3-D Block Stripe Noise Detection and Removal Method Based on Global Search Optimization and Dense Gabor Filters

Weighted Feature Fusion Network Based on Large Kernel Convolution and Transformer for Multi-Modal Remote Sensing Image Segmentation

A DNA-Level Convolutional Neural Network Based on Strand Displacement Reaction for Image Recognition

ATT-CR: Adaptive Triangular Transformer for Cloud Removal

Enhancing Long-Duration Multi-Person Tracking in Hospitality Settings Through Random-Skip Sub-Track Correction

Improving Token-Based Object Detection With Video

SAFH-Net: A Hybrid Network With Shuffle Attention and Adaptive Feature Fusion for Enhanced Retinal Vessel Segmentation

An Efficient Topology Construction Scheme Designed for Graph Neural Networks in Hyperspectral Image Classification

Toward Sustainable Agriculture: DPA-UNet for Semantic Segmentation of Landscapes Using Remote Sensing Imagery

A Spatio-Temporal Attention Network With Multiframe Information for Infrared Small Target Detection

Self Attention GAN and SWIN Transformer-Based Pothole Detection With Trust Region-Based LSM and Hough Line Transform for 2D to 3D Conversion

Squeeze-SwinFormer: Spectral Squeeze and Excitation Swin Transformer Network for Hyperspectral Image Classification

LoFi: Neural Local Fields for Scalable Image Reconstruction

Neurological Disorder Recognition via Comprehensive Feature Fusion by Integrating Deep Learning and Texture Analysis

LRFL-YOLO: A Large Receptive Field and Lightweight Model for Small Object Detection

Learning With Partial-Label and Unlabeled Data: Contrastive With Negative Example Separation

Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance Learning

Mapping Spatio-Temporal Dynamics of Offshore Targets Using SAR Images and Deep Learning

High Quality Dynamic Occlusion Computational Ghost Imaging Guided Through Conditional Diffusion Model

XPolypNet: A U-Net-Based Model for Semantic Segmentation of Gastrointestinal Polyps With Explainable AI

YOLOv5-MDS: Target Detection Model for PCB Defect Inspection Based on YOLOv5 Integrated With Mamba Architecture

Data Quality Analyses for Automatic Aerial Thermography Inspection of PV Power Plants

Autism spectrum disorder detection using parallel DCNN with improved teaching learning optimization feature selection scheme

Highlight Removal From Wireless Capsule Endoscopy Images

Brain-Shapelet: A Framework for Capturing Instantaneous Abnormalities in Brain Activity for Autism Spectrum Disorder Diagnosis

An Automated Method Inspired by Taxonomic Classification for Distinguishing Chilean Pelagic Fish Species

DADSR: Degradation-Aware Diffusion Super-Resolution Model for Object-Level SAR Image

SN360: Semantic and Surface Normal Cascaded Multi-Task 360 Monocular Depth Estimation

Depth Inversion Using SAR and Super-Resolution Enhancement: A Case Study on Case II Waters

Frequency Spectrum Adaptor for Remote Sensing Image–Text Retrieval

Attention-Based Dual-Knowledge Distillation for Alzheimer’s Disease Stage Detection Using MRI Scans


A Temporal–Spatial–Spectral Fusion Framework for Coastal Wetland Mapping on Time-Series Remote Sensing Imagery

RFHS-RTDETR: Multi-Domain Collaborative Network With Hierarchical Feature Integration for UAV-Based Object Detection

DB-Net: A Dual-Branch Hybrid Network for Stroke Lesion Segmentation on Non-Contrast CT Images

SuperCoT-X: Masked Hyperspectral Image Modeling With Diverse Superpixel-Level Contrastive Tokenizer

LS-YOLO: A Lightweight, Real-Time YOLO-Based Target Detection Algorithm for Autonomous Driving Under Adverse Environmental Conditions

Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping

PAdaptCD: Progressive Adaptive Thresholding and Bitemporal Image Augmentation for Semisupervised Change Detection

DAM-Net: Domain Adaptation Network With Microlabeled Fine-Tuning for Change Detection

Transformer-Guided Serial Knowledge Distillation for High-Precision Anomaly Detection

HyCoViT: Hybrid Convolution Vision Transformer With Dynamic Dropout for Enhanced Medical Chest X-Ray Classification

DFC-Net: Dual-Branch Collaborative Feature Enhancement for Cloud Detection in Remote Sensing Images

Fine-Scale Small Water Body Uncovered by GF-2 Remote Sensing and Multifeature Deep Learning Model

Multisensor Remote Sensing and Advanced Image Processing for Integrated Assessment of Geological Structure and Environmental Dynamics

FR-CapsNet: Enhancing Low-Resolution Image Classification via Frequency Routed Capsules

Energy-Efficient SAR Coherent Change Detection Based on Deep Multithreshold Spiking-UNet


Hyperspectral Pansharpening Enhanced With Multi-Image Super-Resolution for PRISMA Data

Enhancing Water Bodies Detection in the Highland and Coastal Zones Through Multisensor Spectral Data Fusion and Deep Learning

Enhancing Hyperspectral Images Compressive Sensing Reconstruction With Smooth Low-Rankness Joint Gradient Sparsity

Novel Efficient Steel Surface Defect Detection Model Based on ConvNeXt v2 and Squeeze Aggregated Excitation Attention

CSCP-YOLO: A Lightweight and Efficient Algorithm for Real-Time Steel Surface Defect Detection

Radio Frequency Sensing–Based Human Emotion Identification by Leveraging 2D Transformation Techniques and Deep Learning Models

Multi-Scale Information Interaction and Feature Pyramid Network for Salient Object Detection

An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep Learning

TMAR: 3-D Transformer Network via Masked Autoencoder Regularization for Hyperspectral Sharpening

Consistency Preserved Nonuniform Scattering Removal for Wide-Swath Remote Sensing Images


Transfer Learning Between Sentinel-1 Acquisition Modes Enhances the Few-Shot Segmentation of Natural Oil Slicks in the Arctic

Attention-Enhanced CNN for High-Performance Deepfake Detection: A Multi-Dataset Study

Descriptor: Manually Annotated CT Dataset of Lung Lobes in COVID-19 and Cancer Patients (LOCCA)

FUSCANet: Enhancing Skin Disease Classification Through Feature Fusion and Spatial-Channel Attention Mechanisms

Learning Frequency-Aware Spatial Attention by Reconstructing Images With Different Frequency Responses

Evaluation of Post Hoc Uncertainty Quantification Approaches for Flood Detection From SAR Imagery

Hierarchical Multi-Scale Patch Attention and Global Feature-Adaptive Fusion for Robust Occluded Face Recognition

PlantHealthNet: Transformer-Enhanced Hybrid Models for Disease Diagnosis and Severity Estimation in Agriculture

A Fusion Strategy for High-Accuracy Multilayer Soil Moisture Downscaling and Mapping

Multisensor Fusion and Deep Learning Approaches for Semantic Segmentation of Glacial Lakes: A Comparative Study for Coastal Hydrology Applications


Global Structural Knowledge Distillation for Semantic Segmentation

Improved YOLOv5-Based Radar Object Detection

MMTraP: Multi-Sensor Multi-Agent Trajectory Prediction in BEV

Selective Intensity Ensemble Classifier (SIEC): A Triple-Threshold Strategy for Microscopic Malaria Cell Image Classification

Lightweight and Accurate YOLOv7-Based Ensembles With Knowledge Distillation for Urinary Sediment Detection

HistoDX: Revolutionizing Breast Cancer Diagnosis Through Advanced Imaging Techniques

A Hybrid Deep Learning Framework for Early-Stage Alzheimer’s Disease Classification From Neuro-Imaging Biomarkers

Deformable Feature Fusion and Accurate Anchors Prediction for Lightweight SAR Ship Detector Based on Dynamic Hierarchical Model Pruning

Computationally Enhanced UAV-Based Real-Time Pothole Detection Using YOLOv7-C3ECA-DSA Algorithm

Advanced Leaf Classification Using Multi-Layer Perceptron for Smart Crop Management

Cancer Cell Classification From Peripheral Blood Smear Data Using the YOLOv8 Architecture

The Application of Kalman Filter Algorithm in Rail Transit Signal Safety Detection

Row-Column Decoupled Loss: A Probability-Based Geometric Similarity Framework for Aerial Micro-Target Detection

Estimation of Forest Aboveground Biomass Using Multitemporal Quad-Polarimetric PALSAR-2 SAR Data by Model-Free Decomposition Approach in Planted Forest

Enhancing Food Security With High-Quality Land-Use and Land-Cover Maps: A Local Model Approach

MEIS-YOLO: Improving YOLOv11 for Efficient Aerial Object Detection with Lightweight Design

DeepSeqCoco: A Robust Mobile Friendly Deep Learning Model for Detection of Diseases in Cocos Nucifera


Enhancing Fabric Defect Detection With Attention Mechanisms and Optimized YOLOv8 Framework

R-YOLO: Enhancing Takeoff/Landing Safety in UAM Vertiports With Deep Learning Model

BD-WNet: Boundary Decoupling-Based W-Shape Network for Road Segmentation in Optical Remote Sensing Imagery

ITT: Long-Range Spatial Dependencies for Sea Ice Semantic Segmentation

A Novel Context-Aware Feature Pyramid Networks With Kolmogorov-Arnold Modeling and XAI Framework for Robust Lung Cancer Detection

Self- and Cross-Attention Enhanced Transformer for Visible and Thermal Infrared Hyperspectral Image Classification

An Integrated Sample-Free Method for Agricultural Field Delineation From High-Resolution Remote Sensing Imagery

Assessing the Detection Capabilities of RGB and Infrared Models for Robust Occluded and Unoccluded Pedestrian Detection

A Novel Hybrid Architecture With Fast Lightweight Encoder and Transformer Under Attention Fusion for the Enhancement of Sand Dust and Haze Image Restoration

A Hierarchical Feature Fusion and Dynamic Collaboration Framework for Robust Small Target Detection

Defect Detection Algorithm for Electrical Substation Equipment Based on Improved YOLOv10n

Emotion-Based Music Recommendation System Integrating Facial Expression Recognition and Lyrics Sentiment Analysis

Fused YOLO and Traditional Features for Emotion Recognition From Facial Images of Tamil and Russian Speaking Children: A Cross-Cultural Study

Corrections to “Research on Underwater Small Target Detection Technology Based on Single-Stage USSTD-YOLOv8n”

Osteosarcoma CT Image Segmentation Based on OSCA-TransUnet Model

Dam Crack Instance Segmentation Algorithm Based on Improved YOLOv8


TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation

Improved YOLOv8 Algorithm was Used to Segment Cucumber Seedlings Under Complex Artificial Light Conditions

M$^{2}$Convformer: Multiscale Masked Hybrid Convolution-Transformer Network for Hyperspectral Image Super-Resolution

MultiSHTM: Multi-Level Attention Enabled Bi-Directional Model for the Summarization of Chart Images

An Efficient Encoding Spectral Information in Hyperspectral Images for Transfer Learning of Mask R-CNN for Instance Segmentation of Tomato Sepals

A Weighted Low-Rank and Sparse Constraint-Based Multichannel Radar Forward-Looking Imaging Method

High-Performance Lung Disease Identification and Explanation Using a ReciproCAM-Enhanced Lightweight Convolutional Neural Network

Contextual Tomographic SAR Denoising Approach for Estimating Scatterers’ Height and Deformation Velocity

Enhancing Bounding Box Regression for Object Detection: Dimensional Angle Precision IoU-Loss

Statistical Performance Evaluation of the Deep Learning Architectures Over Body Fluid Cytology Images

MulFF-Net: A Domain-Aware Multiscale Feature Fusion Network for Breast Ultrasound Image Segmentation With Radiomic Applications

Real-Time Object Detection Using Low-Resolution Thermal Camera for Smart Ventilation Systems

Hybrid Dual-Input Model for Respiratory Sound Classification With Mel Spectrogram and Waveform

Sign Language Recognition—Dataset Cleaning for Robust Word Classification in a Landmark-Based Approach

A Two-Stage U-Net Framework for Interactive Segmentation of Lung Nodules in CT Scans

A Computer Vision and Point Cloud-Based Monitoring Approach for Automated Construction Tasks Using Full-Scale Robotized Mobile Cranes

A Dual-Purpose Microwave-Optical Component for Wireless Capsule Endoscopy: A Feasibility Study by Radio Link Analysis

CASSNet: Cross-Attention Enhanced Spectral–Spatial Interaction Network for Hyperspectral Image Super-Resolution

Peduncle Detection of Ripe Strawberry to Localize Picking Point Using DF-Mask R-CNN and Monocular Depth

Graph-Aware Multimodal Deep Learning for Classification of Diabetic Retinopathy Images

kLCRNet: Fast Road Network Extraction via Keypoint-Driven Local Connectivity Exploration

NetraAadhaar: A Deep Learning-Driven Aadhaar Verification Platform for the Aid of Visually Impaired

Benchmarking Deep Learning for Wetland Mapping in Denmark Using Remote Sensing Data
Published on: Apr 2025
BorB: A Novel Image Segmentation Technique for Improving Plant Disease Classification With Deep Learning Models

Swin Transformer and Momentum Contrast (MoCo) in Leukemia Diagnostics: A New Paradigm in AI-Driven Blood Cell Cancer Classification



iYOLOV7-TPE-SS: Leveraging Improved YOLO Model With Multilevel Hyperparameter Optimization for Road Damage Detection on Edge Devices

Retinal Image Analysis for Heart Disease Risk Prediction: A Deep Learning Approach

Spectral-Spatial Collaborative Pretraining Framework With Multiconstraint Cooperation for Hyperspectral–Multispectral Image Fusion

Intraoperative Surgical Navigation and Instrument Localization Using a Supervised Learning Transformer Network

Content-Based Image Retrieval for Multi-Class Volumetric Radiology Images: A Benchmark Study

A Super-Resolution Approach for Image Resizing of Infant Fingerprints With Vision Transformers


An Automated Framework of Superpixels-Saliency Map and Gated Recurrent Unit Deep Convolutional Neural Network for Land Cover and Crops Disease Classification

Enhancing Situational Awareness: Anomaly Detection Using Real-Time Video Across Multiple Domains

A Two-Stream Deep Learning Framework for Robust Coral Reef Health Classification: Insights and Interpretability

A Blur-Score-Guided Region Selection Method for Airborne Aircraft Detection in Remote Sensing Images


Forest Fire Detection Based on Enhanced Feature Information Capture and Long-Range Dependency

A Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation Model

A Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and Tracking

UAV High-Speed Target Reconnaissance and Deblurring


Performance Evaluation of Image Super-Resolution for Cavity Detection in Irradiated Materials

Satellite Image Inpainting With Edge-Conditional Expectation Attention

CD-STMamba: Toward Remote Sensing Image Change Detection With Spatio-Temporal Interaction Mamba Model



Enhanced Multi-Pill Detection and Recognition Using VFI Augmentation and Auto-Labeling for Limited Single-Pill Data

Deep Fusion of Neurophysiological and Facial Features for Enhanced Emotion Detection

MR Spatiospectral Reconstruction Integrating Subspace Modeling and Self-Supervised Spatiotemporal Denoising

MFDAFF-Net: Multiscale Frequency-Aware and Dual Attention-Guided Feature Fusion Network for UAV Imagery Object Detection

Winograd Transform-Based Fast Detection of Heart Disease Using ECG Signals and Chest X-Ray Images

Efficient-Proto-Caps: A Parameter-Efficient and Interpretable Capsule Network for Lung Nodule Characterization


Integrating Random Forest With Boundary Enhancement for Mapping Crop Planting Structure at the Parcel Level From Remote Sensing Images

ESFormer: A Pillar-Based Object Detection Method Based on Point Cloud Expansion Sampling and Optimised Swin Transformer


Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers

YOLO-GCOF: A Lightweight Low-Altitude Drone Detection Model

Adaptive Token Mixer for Hyperspectral Image Classification

Real-Time Long-Wave Infrared Semantic Segmentation With Adaptive Noise Reduction and Feature Fusion


Cross-Modality Object Detection Based on DETR

Vehicle-to-Infrastructure Multi-Sensor Fusion (V2I-MSF) With Reinforcement Learning Framework for Enhancing Autonomous Vehicle Perception

Constructing a Lightweight Fire and Smoke Detection Through the Improved GhostNet Architecture and Attention Module Mechanism

Optimizing Stroke Recognition With MediaPipe and Machine Learning: An Explainable AI Approach for Facial Landmark Analysis

Tongue Image Segmentation Method Based on the VDAU-Net Model

Edge-YOLO: Lightweight Multi-Scale Feature Extraction for Industrial Surface Inspection

Cross-Modal Semantic Relations Enhancement With Graph Attention Network for Image-Text Matching

Transforming Highway Safety With Autonomous Drones and AI: A Framework for Incident Detection and Emergency Response

A Novel Hybrid Model for Brain Ischemic Stroke Detection Using Feature Fusion and Convolutional Block Attention Module

DAF-Net: Dual-Aperture Feature Fusion Network for Aircraft Detection on Complex-Valued SAR Image

DUAL-GDFQ: A Dual-Generator, Dual-Phase Learning Approach for Data-Free Quantization


Enhanced Nighttime Vehicle Detection for On-Board Processing

Near Miss Detection Using Distancing Monitoring and Distance-Based Proximal Indicators

Cross-Scale Transformer-Based Matching Network for Generalizable Person Re-Identification

Estimation of Road Pavement Surface Conditions via Time Series of Satellite Synthetic Aperture Radar Images

Optimized Epoch Selection Ensemble: Integrating Custom CNN and Fine-Tuned MobileNetV2 for Malimg Dataset Classification

Autonomous Aerial Vehicle Object Detection Based on Spatial Perception and Multiscale Semantic and Detail Feature Fusion

Vision Foundation Model Guided Multimodal Fusion Network for Remote Sensing Semantic Segmentation


Integrate the Temporal Scheme for Unsupervised Video Summarization via Attention Mechanism

FRORS: An Effective Fine-Grained Retrieval Framework for Optical Remote Sensing Images

A Comparative Study of Image Processing Techniques for Javanese Ancient Manuscripts Enhancement

DAFDM: A Discerning Deep Learning Model for Active Fire Detection Based on Landsat-8 Imagery

Design of Enhanced License Plate Information Recognition Algorithm Based on Environment Perception

Self-DSNet: A Novel Self-ONNs Based Deep Learning Framework for Multimodal Driving Distraction Detection


High Precision Infant Facial Expression Recognition by Improved YOLOv8

Explainable Mapping of the Irregular Land Use Parcel With a Data Fusion Deep-Learning Model



Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification

LEU3: An Attention Augmented-Based Model for Acute Lymphoblastic Leukemia Classification

Transformative Transfer Learning for MRI Brain Tumor Precision: Innovative Insights

Online Hand Gesture Recognition Using Semantically Interpretable Attention Mechanism

ELTrack: Events-Language Description for Visual Object Tracking

A Single-Stage Photovoltaic Module Defect Detection Method Based on Optimized YOLOv8

Estimating Near-Surface Air Temperature From Satellite-Derived Land Surface Temperature Using Temporal Deep Learning: A Comparative Analysis

GLF-NET: Global and Local Dynamic Feature Fusion Network for Real-Time Steel Strip Surface Defect Detection

Human Pose Estimation and Event Recognition via Feature Extraction and Neuro-Fuzzy Classifier


Attention Enhanced InceptionNeXt-Based Hybrid Deep Learning Model for Lung Cancer Detection

AEFFNet: Attention Enhanced Feature Fusion Network for Small Object Detection in UAV Imagery

Vehicle Detection and Tracking Based on Improved YOLOv8

An Inverted Residual Cross Head Knowledge Distillation Network for Remote Sensing Scene Image Classification

Design of an Integrated Model for Video Summarization Using Multimodal Fusion and YOLO for Crime Scene Analysis

Ultrasound Segmentation Using Semi-Supervised Learning: Application in Point-of-Care Sarcopenia Assessment

Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches

SqueezeSlimU-Net: An Adaptive and Efficient Segmentation Architecture for Real-Time UAV Weed Detection


Knowledge Distillation in Object Detection for Resource-Constrained Edge Computing

DCKD: Distribution-Corrected Knowledge Distillation for Enhanced Industrial Defect Detection

CenterNet-Elite: A Small Object Detection Model for Driving Scenario


Multiscale Feature Fusion for Salient Object Detection of Strip Steel Surface Defects

Multi-Modal Social Media Analytics: A Sentiment Perception-Driven Framework in Nanjing Districts

UVtrack: Multi-Modal Indoor Seamless Localization Using Ultra-Wideband Communication and Vision Sensors

EMSNet: Efficient Multimodal Symmetric Network for Semantic Segmentation of Urban Scene From Remote Sensing Imagery

Unsupervised Image Super-Resolution for High-Resolution Satellite Imagery via Omnidirectional Real-to-Synthetic Domain Translation

Improving Autonomous Vehicle Cognitive Robustness in Extreme Weather With Deep Learning and Thermal Camera Fusion

Hybrid Intersection Over Union Loss for a Robust Small Object Detection in Low-Light Conditions



Satellite-Based Forest Stand Detection Using Artificial Intelligence


Transformer-Based Person Detection in Paired RGB-T Aerial Images With VTSaR Dataset

Application of CRNN and OpenGL in Intelligent Landscape Design Systems Utilizing Internet of Things, Explainable Artificial Intelligence, and Drone Technology

Transformer-Based Multi-Player Tracking and Skill Recognition Framework for Volleyball Analytics

Detection and Classification Method for Early-Stage Colorectal Cancer Using Dyadic Wavelet Packet Transform



Always Clear Days: Degradation Type and Severity Aware All-in-One Adverse Weather Removal

Task-Decoupled Learning Strategies for Optimized Multiclass Object Detection From VHR Optical Remote Sensing Imagery

Multiscale Adapter Based on SAM for Remote Sensing Semantic Segmentation

FedDrip: Federated Learning With Diffusion-Generated Synthetic Image

EfficientNet-b0-Based 3D Quantification Algorithm for Rectangular Defects in Pipelines

RMHA-Net: Robust Optic Disc and Optic Cup Segmentation Based on Residual Multiscale Feature Extraction With Hybrid Attention Networks

An Improved Correlation Filtering Method for Tracking Maritime Small Targets of GF-4 Staring Satellite Sequence Images

Unsupervised Visual-to-Geometric Feature Reconstruction for Vision-Based Industrial Anomaly Detection

Real-time recognition and translation of Kinyarwanda sign language into Kinyarwanda text
Image Processing Final Year Projects - Key Algorithm Used
An advanced iteration of the real-time object detection paradigm focusing on enhanced multi-scale feature fusion, making it highly suitable for image processing projects for final year that require high-speed inference and precise localization in IEEE-aligned research pipelines.
Hybrid models integrate convolutional feature extraction with transformer-based contextual reasoning, improving robustness and generalization across complex image analysis tasks commonly explored in image processing projects literature.
Transformer-driven image processing architectures employ self-attention mechanisms to model global visual context, enabling scalable representation learning and evaluation-ready system design aligned with IEEE research practices for image processing final year projects.
Utilizes compound scaling to balance depth, width, and resolution for optimized feature extraction, and is widely cited in IEEE journals for achieving high accuracy with reduced computational overhead in experimental setups.
A nested U-Net architecture designed for fine-grained medical image segmentation tasks. Its architectural significance is validated through rigorous experimental setups in biomedical imaging research reported across image processing IEEE projects publications.
Addresses the vanishing gradient problem in deep vision pipelines through the implementation of skip connections, remaining a primary benchmark methodology for image classification across IEEE research.
Employed for high-fidelity image synthesis and restoration tasks within modern system architectures, these models are fundamental to IEEE research areas focusing on super-resolution and data augmentation pipelines.
Image Processing IEEE Projects - Wisen TMER-V Methodology
T — Task What primary task (& extensions, if any) does the IEEE journal address?
- Image Processing research tasks focus on systematic visual data analysis under controlled and real-world conditions relevant to image processing projects for final year.
- Tasks are defined at a domain level to ensure reproducibility, scalability, and evaluation consistency across studies.
- Image enhancement and restoration tasks addressing noise, blur, and resolution variations
- Image segmentation and object delineation under varying illumination and scale
- Feature extraction and representation learning for visual recognition
- Image classification and detection with benchmark-driven evaluation
M — Method What IEEE base paper algorithm(s) or architectures are used to solve the task?
- IEEE-aligned Image Processing methods emphasize algorithmic rigor and modular pipeline design commonly adopted in image processing projects.
- Methodological families are selected based on their suitability for quantitative evaluation and comparative analysis.
- Convolutional and attention-based visual representation learning
- Hybrid modeling combining local and global feature reasoning
- Graph-structured and probabilistic formulations for relational modeling
- Multi-stage processing pipelines integrating preprocessing and inference
E — Enhancement What enhancements are proposed to improve upon the base paper algorithm?
- Enhancement strategies improve robustness, generalization, and evaluation stability across image processing final year projects.
- IEEE literature reports enhancement patterns that are reusable across multiple image processing tasks.
- Multi-scale feature integration for improved spatial representation
- Attention-based refinement for contextual consistency
- Hybrid architectural combinations for balanced performance
- Uncertainty-aware modeling to enhance reliability
R — Results Why do the enhancements perform better than the base paper algorithm?
- Domain-level results emphasize measurable performance gains validated through standardized protocols in image processing ieee projects.
- Improvements are reported in terms of accuracy, robustness, and computational efficiency.
- Consistent accuracy improvements across benchmark datasets
- Reduced error rates under noisy or complex visual conditions
- Improved generalization across datasets and task variations
- Stable convergence and reproducible experimental outcomes
V — Validation How are the enhancements scientifically validated?
- Validation practices in Image Processing research follow strict IEEE evaluation standards.
- Experimental setups prioritize reproducibility, comparability, and metric-driven assessment.
- Benchmark dataset evaluation with standardized splits
- Quantitative metrics such as accuracy, precision, recall, and error measures
- Cross-validation and ablation-based analysis
- Comparative benchmarking against established baselines
Image Processing Projects - Libraries & Frameworks
TensorFlow is widely used in image processing research for building scalable computational graphs that support model training, experimentation, and evaluation. Its flexibility enables reproducible pipeline design aligned with IEEE-aligned experimental practices and system development in image processing projects.
PyTorch supports dynamic computation and rapid prototyping of image processing models, making it suitable for research-grade experimentation, ablation studies, and evaluation-centric system development reported in IEEE literature for image processing projects for final year.
OpenCV provides foundational image processing operations that support preprocessing, transformation, and low-level visual analysis, forming a reliable base layer in research-oriented pipelines for image processing final year projects.
This library is used for advanced image manipulation and scientific analysis, focusing on traditional processing techniques aligned with academic standards. It plays a critical role in research-grade systems where precise mathematical filtering and morphological operations are required for experimental validation.
This parallel computing platform is essential for accelerating the training and inference of deep vision models on GPU hardware. Its role in modern research is highlighted by its ability to handle the intensive computational requirements of image processing ieee projects and large-scale training pipelines.
ONNX facilitates model interoperability and deployment validation across heterogeneous environments, enabling reproducible evaluation and architectural comparison in IEEE-aligned image processing research.
Image Processing IEEE Projects - Real World Applications
Medical image analysis focuses on extracting clinically relevant information from imaging data such as scans and microscopy images. In image processing projects for final year, IEEE-aligned systems employ structured preprocessing, feature extraction, and validation pipelines to ensure accuracy and reproducibility in diagnostic support environments.
This application involves analyzing satellite and aerial imagery for environmental monitoring and geospatial assessment. Implementations follow IEEE methodologies using scalable architectures and standardized evaluation metrics to handle high-resolution and multi-spectral visual data commonly studied in image processing ieee projects.
Drone-based multispectral imaging assesses crop health and soil moisture levels across large-scale farms to optimize agricultural yield. These applications use IEEE-standardized vegetation indices and remote sensing methodologies for pixel-level classification and temporal analysis, making them suitable for applied image processing final year projects.
Industrial inspection systems apply image processing to detect defects and anomalies in manufacturing processes. Research-grade implementations emphasize robust feature representation, real-time processing pipelines, and quantitative evaluation under varying operational conditions.
Surveillance applications analyze visual data for object detection, tracking, and scene interpretation. IEEE research emphasizes multi-stage processing architectures and evaluation-driven validation to ensure reliability across complex and dynamic visual environments, positioning them as advanced extensions within image processing projects for final year.
Image Processing Final Year Projects - Conceptual Foundations
Image Processing as a research domain focuses on the systematic transformation and analysis of visual data to extract structured information using computational and mathematical models. Conceptually, the domain emphasizes signal representation, spatial transformations, feature abstraction, and evaluation-driven system design aligned with IEEE research methodologies and standardized validation practices commonly adopted in image processing projects for final year.
Within academic and research workflows, Image Processing research is guided by structured experimentation and metric-based evaluation. Emphasis is placed on reproducibility, benchmark-driven validation, and comparative analysis, ensuring consistent assessment of algorithmic effectiveness across diverse visual conditions and datasets expected in image processing ieee projects and journal-oriented postgraduate research environments.
Conceptually, Image Processing forms a foundational layer for several adjacent IEEE research domains, including Generative AI and Deep Learning, where visual representation learning, hierarchical feature modeling, and evaluation-centric system design extend core image processing principles into broader intelligent system architectures.
Image Processing Projects for Final Year - Why Choose Wisen
IEEE Journal Alignment
Our implementation framework strictly follows IEEE 2025–2026 journal methodologies, ensuring that your image processing projects for final year meet the high academic standards required for peer-reviewed research publications.
100% Working Code Assurance
We provide end-to-end support with a 100% working project guarantee. Every Image Processing architecture is rigorously tested for deployment readiness, ensuring zero-error execution during final-year project demonstrations.
Research-Grade Datasets
Wisen utilizes standardized IEEE-recognized datasets and high-resolution visual data cohorts. We guide students in proper data curation, augmentation, and preprocessing techniques essential for building robust image processing final year projects with statistically significant experimental results.
Technical Accuracy & PSNR Validation
Projects are evaluated using advanced vision metrics such as PSNR, SSIM, and mAP. This quantitative approach ensures that your final-year project output is not just functional but academically superior in technical performance.
Expert Research Mentoring
Receive guidance from experienced research scholars who specialize in computer vision and signal processing. Our mentoring focuses on architectural innovation and depth, helping you master the complex mathematical foundations of modern image processing.

Image Processing Projects for Final Year - IEEE Research Areas
Research in this area focuses on the automated extraction of anatomical structures and pathological regions from complex volumetric data. The primary challenge involves managing high-dimensional data with significant noise and low contrast, where precise boundary localization is critical for clinical diagnostic accuracy. The scope encompasses the development of robust models capable of generalizing across heterogeneous imaging modalities and sensor-specific variations, making this area central to image processing projects for final year.
Implementation typically leverages nested encoder–decoder architectures and attention-gated neural networks to capture both local spatial features and global contextual dependencies. Methodological rigor is maintained through specialized loss functions such as Dice and generalized Tversky loss, with evaluation aligned to academic benchmarks commonly reported in image processing ieee projects using metrics like Jaccard Index and Hausdorff Distance.
The research goal in this domain is the interpretation of spectral–spatial information captured via satellite or aerial sensors for land-cover classification and environmental monitoring. Challenges arise from high spectral dimensionality and mixed pixels, which complicate material discrimination.
Systems are implemented using multi-branch convolutional networks and vision transformers that fuse spectral–temporal information with spatial context. Architectural innovations frequently include residual learning and 3D convolutional layers, and validation emphasizes Overall Accuracy (OA) and Kappa Coefficient through cross-scene benchmarking in image processing final year projects.
This research area focuses on reconstructing high-quality images from degraded, low-resolution, or noisy inputs. The objective is to restore perceptual fidelity and structural detail while balancing artifact suppression and edge preservation.
Typical implementations involve adversarial training frameworks and deep residual-in-residual architectures with dense feature fusion. Benchmarking relies on SSIM and PSNR metrics alongside qualitative mean opinion scores, making this a frequent focus within image processing projects for final year.
The scope of this research involves precise localization and classification of multiple entities in dynamic visual environments. Research concentrates on improving detection accuracy for small or occluded objects while optimizing architectures for low-latency inference under resource constraints.
Methodologies center on single-stage and multi-stage detection backbones with feature pyramid networks and path-aggregation blocks. Evaluation follows standardized mean Average Precision (mAP) across multiple IoU thresholds to ensure comprehensive performance assessment.
This area focuses on identifying and verifying individuals using unique physiological visual traits. Research goals emphasize robustness against illumination, pose, and occlusion variations, along with strong anti-spoofing mechanisms.
Implementation relies on deep metric learning and embedding optimization using triplet or contrastive loss functions. Validation protocols involve large-scale biometric datasets and reporting FAR and FRR metrics to establish system reliability.
Image Processing Projects for Students - Career Outcomes
This role focuses on designing and evaluating algorithmic pipelines for visual data analysis in research-oriented environments, closely aligned with image processing projects for final year that demand methodological rigor and experimental validation.
Expertise in evaluation-driven design, benchmarking practices, and system-level experimentation is essential for contributing to reproducible and publication-ready outcomes reported in image processing ieee projects.
Computer vision systems engineers work on end-to-end visual analysis systems that integrate image processing components into larger architectures. IEEE-aligned work prioritizes scalable system design and robust validation under real-world conditions.
The role requires strong understanding of visual representation models, architectural integration, and metric-based evaluation practices common in research and applied systems.
This role applies learning-based methods to image-centric problems, focusing on experimental performance and generalization within image processing final year projects.
IEEE research practices guide model evaluation, comparative analysis, and result interpretation to ensure reproducible experimentation and system reliability.
Visual analytics specialists focus on extracting actionable insights from complex image data through structured processing and analysis pipelines. IEEE-aligned research emphasizes clarity of problem formulation and rigorous evaluation methodologies.
The role demands expertise in feature representation, analysis workflows, and systematic validation across diverse visual datasets.
Research scientists investigate advanced visual intelligence problems that extend traditional image processing into intelligent system design, frequently contributing to image processing projects for final year with strong theoretical grounding.
This role requires deep understanding of research methodologies, evaluation metrics, and the ability to position image processing contributions within broader interdisciplinary research contexts.
Image Processing Projects for Final Year - FAQ
What are some good project ideas in IEEE Image Processing Domain Projects for a final-year student?
Academic implementations focusing on deep learning-based medical imaging, satellite imagery analysis, and real-time object detection frameworks are excellent choices aligned with IEEE 2025–2026 research trends.
What are trending Image Processing final year projects?
Current trends in IEEE research ecosystems emphasize the integration of transformer models for image segmentation, edge-based vision processing pipelines, and enhanced generative adversarial networks for image restoration.
What are top Image Processing projects in 2026?
Top 2026 projects include automated disease classification using neural architecture search and hyperspectral image processing for environmental monitoring, derived from recent IEEE journal publications.
Is the Image Processing domain suitable or best for final-year projects?
The domain is ideal as it allows students to develop highly visible outputs while implementing rigorous IEEE methodologies and validation protocols suitable for peer-reviewed journal extensions.
How does the Wisen proposed system validate image processing accuracy?
The Wisen implementation pipeline utilizes standardized IEEE metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Intersection over Union (IoU) to provide experimental evaluation rigor.
What architectures are utilized in Wisen Image Processing implementations?
Wisen proposed architecture integrates state-of-the-art Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and attention-gated U-Net structures to ensure high technical accuracy and research readiness.
Are these implementations suitable for MTech and PhD research?
Yes, our domain-level research directions focus on scalability and architectural innovation, making them suitable for advanced research paper writing and IEEE conference and journal submissions.
Does the system support real-world deployment for image analysis?
Wisen system is designed for practical implementation, addressing real-world constraints such as computational latency and data heterogeneity observed in IEEE industry-aligned publications.
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Stop worrying about your project output. We provide complete IEEE 2025–2026 journal-based final year project implementation support, from abstract to code execution, ensuring you become industry-ready.



