Image Processing Projects for ECE - IEEE Aligned Implementation
Image Processing Projects for ECE focus on software-based image analysis systems aligned with IEEE research practices. These projects emphasize algorithmic modeling, mathematical transformations, and simulation-driven experimentation for image enhancement, restoration, and representation learning tasks.
The implementation scope prioritizes reproducible software pipelines where image processing algorithms are evaluated using standardized metrics. Systems are designed to support analytical validation, performance benchmarking, and controlled experimentation without any hardware dependency.
Image Processing IEEE Projects for ECE - IEEE 2026 Journals


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 Projects for Final Year ECE - Key Algorithms Used
Vision Transformer processes images as sequences of patches using self-attention to capture global contextual information. In ECE software-based image processing projects, ViT is used for image classification and representation analysis in simulation environments.
Evaluation focuses on attention stability, classification accuracy, and robustness across benchmark image datasets under IEEE-aligned validation protocols.
Swin Transformer introduces hierarchical feature learning through shifted window attention for efficient image processing. ECE projects adopt this algorithm for scalable image analysis and feature extraction tasks.
Performance validation emphasizes multi-scale feature consistency, computational efficiency, and accuracy under controlled simulation conditions.
Diffusion models generate images via iterative noise removal, enabling high-fidelity image synthesis and reconstruction. In image processing projects for ECE, these models are applied for image restoration and enhancement studies.
Evaluation includes convergence behavior, perceptual quality metrics, and reconstruction accuracy using standardized datasets.
Masked Autoencoders learn visual representations by reconstructing masked portions of images, supporting self-supervised learning. ECE image processing projects use MAE for feature learning and image restoration analysis.
Validation focuses on reconstruction quality, representation robustness, and generalization across image datasets.
Deep Image Prior exploits convolutional network structure as an implicit prior for image restoration without training data. ECE projects use this algorithm for analytical denoising and restoration experiments.
Evaluation emphasizes restoration quality, convergence trends, and comparative performance against data-driven models.
Image Processing IEEE Projects for ECE - Wisen TMER-V Methodology
T — Task What primary task (& extensions, if any) does the IEEE journal address?
- Define image processing problem statements focused on enhancement, restoration, segmentation, and analytical interpretation.
- Formulate objectives for software-based image analysis using mathematical modeling and simulation-driven environments.
- Image representation and transformation
- Noise reduction and enhancement objectives
- Analytical image interpretation tasks
M — Method What IEEE base paper algorithm(s) or architectures are used to solve the task?
- Adopt IEEE-aligned image processing methodologies widely used in domain-level research.
- Implement algorithms as reproducible software pipelines for controlled experimentation and evaluation.
- Spatial and frequency domain processing
- Learning-based image analysis models
- Algorithmic feature extraction techniques
E — Enhancement What enhancements are proposed to improve upon the base paper algorithm?
- Enhance image quality and analytical performance through algorithm refinement and parameter optimization.
- Improve robustness and consistency of processing pipelines under varying image conditions.
- Contrast and resolution improvement
- Noise suppression optimization
- Feature clarity enhancement
R — Results Why do the enhancements perform better than the base paper algorithm?
- Demonstrate measurable improvements in image quality and analytical accuracy.
- Present results across multiple datasets to ensure stability and generalization.
- Improved visual quality metrics
- Stable analytical performance
- Consistent processing outcomes
V — Validation How are the enhancements scientifically validated?
- Validate image processing systems using standardized IEEE evaluation practices.
- Ensure reproducibility and benchmark-driven comparison of results.
- Image quality and error metrics
- Robustness and consistency analysis
- Reproducibility verification
Latest Image Processing Projects for ECE Students - Software Tools and Libraries
OpenCV provides a comprehensive set of software-based image processing functions for filtering, transformation, and feature extraction. Image processing projects for ECE use OpenCV to build simulation-driven analysis pipelines for algorithm evaluation.
Performance assessment focuses on numerical accuracy, processing efficiency, and reproducibility across benchmark datasets.
PyTorch supports flexible experimentation with deep image processing models using dynamic computation graphs. ECE image processing projects use PyTorch for implementing transformers, diffusion models, and convolutional pipelines.
Evaluation emphasizes training stability, convergence behavior, and analytical correctness.
TensorFlow enables scalable development and evaluation of image processing systems in controlled software environments. ECE projects apply TensorFlow for structured experimentation and performance benchmarking.
Validation focuses on consistency, convergence reliability, and metric-driven evaluation.
Scikit-Image offers classical image processing algorithms for enhancement, restoration, and segmentation tasks. ECE projects integrate it for analytical comparisons and simulation studies.
Evaluation emphasizes algorithm precision, repeatability, and robustness.
MATLAB provides a simulation-focused environment for implementing and validating image processing algorithms. ECE projects use it for controlled experimentation and comparative analysis.
Evaluation focuses on numerical accuracy and result consistency.
Image Processing IEEE Projects for ECE - Software-Based Applications
Software-based systems improve image quality using filtering and transformation techniques. ECE projects evaluate enhancement pipelines through simulation-based experimentation.
Validation focuses on perceptual quality metrics and numerical improvement measures.
Analytical and learning-based models reconstruct degraded images under controlled conditions. Image processing projects for ECE analyze robustness and convergence behavior.
Evaluation emphasizes reconstruction error and stability metrics.
Image processing systems extract meaningful representations for analytical tasks. ECE projects simulate feature learning pipelines using software-based models.
Validation includes consistency, generalization, and reproducibility metrics.
Software-based segmentation systems partition images into meaningful regions for analytical studies. ECE projects focus on segmentation accuracy in simulation environments.
Evaluation emphasizes boundary precision and repeatability.
Generative image models produce synthetic datasets for analytical experimentation. ECE projects study statistical consistency and distribution alignment.
Validation focuses on realism measures and dataset fidelity.
Image Processing Projects for ECE - Conceptual Foundations
Conceptually, image processing projects for ECE are grounded in mathematical transformations, signal representation, and algorithmic analysis implemented entirely through software-based systems. The focus is on how images are modeled, enhanced, and interpreted using analytical pipelines rather than physical acquisition hardware.
From a system perspective, these projects emphasize reproducible experimentation, evaluation metrics, and performance benchmarking aligned with IEEE research practices. Conceptual understanding prioritizes simulation-driven validation and analytical rigor over deployment considerations.
Closely related ECE software domains that complement image processing system design include Deep Learning Projects for ECE Students, Machine Learning Projects for ECE Students, and Networking Projects for ECE Students.
Image Processing Projects for ECE - Why Choose This Domain
Image Processing Projects for ECE are designed as software-only analytical systems that align strongly with the mathematical and algorithmic foundations of Electronics and Communication Engineering.
Strong IEEE Research Foundation
The image processing domain is extensively supported by IEEE journals, offering well-defined algorithms, benchmarking datasets, and evaluation metrics suitable for research-grade implementations.
Pure Software and Simulation Orientation
All image processing systems are implemented using software-based pipelines, enabling simulation-driven experimentation without reliance on hardware or embedded platforms.
High Analytical and Mathematical Depth
Projects emphasize transformations, feature extraction, and representation learning, allowing ECE students to apply signal-processing concepts in a modern software context.
Seamless Integration With Advanced AI Methods
Image processing projects integrate naturally with deep learning, generative modeling, and machine learning techniques widely used in IEEE research.
Long-Term Research and Career Continuity
The domain provides a strong foundation for higher studies, research roles, and analytical engineering careers that demand evaluation-driven and reproducible system design.

Image Processing Projects for ECE - IEEE Research Areas
Research focuses on learning discriminative and robust representations from images using deep models. IEEE studies emphasize scalability and generalization in software-only environments.
Validation centers on benchmark-driven accuracy and reproducibility analysis.
This research area studies iterative generative processes for image synthesis and restoration. IEEE publications evaluate convergence stability and robustness.
Validation focuses on fidelity metrics and numerical consistency.
Research explores attention-based models for capturing global image context. IEEE studies emphasize analytical performance and scalability.
Evaluation includes attention consistency and accuracy metrics.
This area investigates representation learning without labeled data. IEEE research emphasizes robustness and transferability.
Validation focuses on generalization and stability measures.
Research embeds evaluation mechanisms directly into processing pipelines. IEEE studies emphasize reproducibility and metric-driven validation.
Validation relies on standardized benchmarks and controlled experimentation.
Image Processing Projects for ECE - Career Outcomes
This role focuses on designing, analyzing, and validating image processing algorithms in software environments. ECE graduates work on simulation-driven analytical systems.
Career progression emphasizes research rigor, evaluation methodology, and contribution to advanced studies.
This role involves analytical interpretation of image data using software-based pipelines. ECE graduates apply feature analysis and evaluation techniques.
Career growth emphasizes system reasoning and performance assessment.
This role applies deep image models to analytical and simulation-driven tasks. Image processing projects for ECE provide strong alignment.
Career outcomes focus on algorithm evaluation and optimization.
This role builds and validates simulation-based analytical systems. ECE graduates apply image processing pipelines in controlled environments.
Career growth emphasizes reproducibility and analytical accuracy.
This role bridges data analysis and image modeling within research contexts. ECE graduates apply statistical and analytical techniques.
Career outcomes emphasize methodological rigor and research continuity.
Image Processing Projects for ECE - FAQ
What are some good project ideas in IEEE Image Processing Domain Projects for a final-year student?
IEEE image processing domain projects focus on software-based image analysis, feature extraction, transformation techniques, and evaluation-centric simulation pipelines.
What are trending image processing final year projects?
Trending image processing final year projects emphasize deep image representation, multi-scale analysis, and simulation-driven validation aligned with IEEE methodologies.
What are top image processing projects in 2026?
Top image processing projects in 2026 focus on deep learning–assisted image analysis, evaluation-aware pipelines, and benchmark-driven experimentation.
Is the image processing domain suitable or best for final-year projects?
The image processing domain is suitable for final-year projects due to its strong IEEE research foundation, software-centric scope, and well-defined evaluation metrics.
Do you provide a combo offer for image processing projects?
Yes, a combined package is available that includes project implementation support, documentation guidance, and IEEE paper preparation assistance.
Which algorithms are commonly used in IEEE image processing projects for ECE?
IEEE image processing projects for ECE commonly use convolution-based filtering, transformation techniques, and deep learning–based feature extraction implemented through software simulation pipelines.
How are image processing systems evaluated in IEEE research?
Evaluation emphasizes reconstruction accuracy, feature quality metrics, robustness analysis, and reproducibility using simulation-based experimental setups.
Are image processing projects for ECE fully software-based?
Yes, ECE image processing projects are implemented as fully software-based systems focusing on algorithmic modeling, simulation, and analytical validation without hardware dependency.
What type of datasets are used for image processing projects in ECE?
Datasets typically include standard image benchmarks and domain-specific image collections suitable for algorithmic analysis and simulation-driven evaluation.
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