Introduction

The deep learning methodologies being applied for the image processing needs are much diverse and evolving since the past few years. These deep learning concepts have been prominently utilized for enhancing the efficiency and accuracy of image processing outcomes in a varieties of sectors like automobile, medical care, etc. For instance, multitude of medical images in the healthcare sector are being investigated by deploying deep learning methodologies to recognize abnormalities like tumors with a superior accuracy than a human being could recognize. 

In this article, we will bring out the potential of deep learning in image processing by briefing the importance of image processing domain, role of Convolutional Neural Network (CNN), advanced deep learning projects attempting image processing, and top 50+ image processing project ideas. All the listed image processing projects for ECE students concentrate both on deep learning and image processing concepts.

Must Read: Deep Learning Final Year Projects

Importance of Image Processing domain

As the technologies pertaining to images develop, human beings tend to come across big quantum of image data throughout their lifetime. However, pace at which humans process those images is restricted, which is much slower than the pace of actual image production. As a result, increased level of population has now begun to utilize computers for increasing the pace of image processing and acquiring image data that is not visible to normal eyes of humans. This has paved the way for the adoption and development of state-of-the-art image processing approaches.

Across the genre of image processing, diverse types of approaches are being adopted to carry out actions like feature extraction, enhancement, denoising, classification, and segmentation. These approaches cumulatively can sort out the issues and possibilities posed by several factors of image investigation and administration, empowering uses across numerous sectors. Every one of these approaches have enabled human beings to redefine the perception of images, extract necessary data, and make enlightened decisions according to the visually observed data.

Also Read: MATLAB Projects on Image Processing

Role of CNN in Image Processing applications

Deep learning is commonly deployed for image processing owing to a few benefits like raised accuracy and powerful feature extraction capability. Convolutional Neural Network (CNN) is one of the much significant elements of deep learning frameworks, which is being used in image processing. These CNNs are primarily constructed for analyzing the visually observable data and have potential of recognizing attributes and complex pattern that is embedded inside the images. It comprises several levels of interlinked nodes, with every level being accountable for identifying different factors in an image, namely, shapes, textures, edges, and many more. When compared to a few benchmark networks like Recurrent Neural Network (RNN), CNN is able to leave notable impact over image processing applications including the recognition. 

Specifically, CNNs are much robust in recognizing image since it could autonomously acquire suitable attributes from the data given as the input alongside the elimination of requirement for a manually carried out attribute engineering. CNN permits the approaches to be flexible to a numerous visual data input and to recognize the entities in an efficient way, even under sophisticated and disorderly scenarios.

Check About: Best Deep Learning Projects

Advanced applications of Deep learning in Image Processing

The most crucial growth of deep learning in the arena of image processing is the image classification job in a typical ImageNet problem. For accomplishment of ImageNet classification, the convolutional networks are predominantly utilized. In majority of instances, the information acquired by the conventional machine learning approaches frequently give rise to raised level of errors and the rate of error on the test set is considerably higher. Favorably, adoption of advanced deep learning convolutional networks like VGG and AlexNet are able to sort out the issues faced in case of image processing endeavors using conventional machine learning approaches. Advanced deep learning projects usually adopt advanced frameworks for better image processing outcomes.

Refer About: Advanced Deep Learning Projects

Top 50+ Image Processing Final Year Projects

In this era where many Engineering students get attracted towards the digital image processing projects, it is necessary to make use of suitable and effective approaches for the successful conduct of their projects. So, top 50+ image processing final year projects (particularly across deep learning in image processing) have been curated and mentioned by including but not limited to the genres like denoising in image processing, MATLAB projects on image processing, segmentation in image processing, classification in image processing, enhancement, etc.

• Segmentation and Classification of Melanoma Skin Cancer using Deep Learning Techniques 

• Weed Identification Using Deep Learning and Image Processing in Vegetable Plantation 

• Deep Convolutional Encoder-decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation

• Harnessing AI for Deep Fake Detection in Images

• Deep Learning Applications in Medical Image Analysis

• Mushroom Image Classification with CNN

• Detection of Cardiovascular Diseases in ECG Images using Machine Learning and Deep Learning Methods

• Image Based Indian Monument Recognition Using Convoluted Neural Networks (With Aws) 

• Coconut Disease Prediction System Using Image Processing and Deep Learning Techniques 

• Lung, Chest, Skin, Brain and Breast Image Classification

• Physics-Based Generative Adversarial Models for Image Restoration and Beyond 

• Sensitive Nets Learning Agnostic Representations with Application to Face Images 

• Vitamin Deficiency Detection Using Image Processing and Neural Network. 

• Detection of Knee Orthopedic X-ray Images using Deep Learning

• Medical Image segmentation using UNet Architecture

• Image Based Bird Species Identification Using CNN

• Discriminative Feature Learning for Thorax Disease Classification in Chest X-Ray Images 

• Video Image Deblurring Algorithm Based on OpenCV

• Segmentation of Retinal Lesions in Fundus Images using Auto Encoder-Decoder Architecture. 

• Image Denoising using Auto Encoders and Decoders

• Micro Organism Image Recognition and Disease Prediction Based on DL 

• Image Based Plant Leaf Disease Detection Using Transfer Learning Plant Name-Hibiscus 

• Automatic Number Plate Detection by using Image Processing 

• Deep Learning Based Cervical Cancer Diagnosis Using Time-Lapsed Colposcopic Images 

• Multi scale progressive fusion network for single image deraining

• A Novel Algorithm for Kidney Stone Detection Using Image Processing Techniques 

• CNN Based Study of Improvised Food Image Classification

• Human Age and Gender Estimation using Convolutional Neural Network and Image Processing

• Skin Cancer Classification using Image Processing and Machine Learning 

• Image Recognition and Enhancement using Multi-Scale Retinex and Histogram Equalization 

• Enhanced YOLO v3 Tiny Network for Real-Time Ship Detection from Visual Image 

• COVID 19, Pneumonia and Other Disease Classification using Chest X-Ray images 

• Smart Agricultural Robot for Spraying Pesticide with Image Processing based Disease Classification Technique

• Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

• Deblocking the Image Using Convolutional Neural Networks

• Detecting Tampered Regions in JPEG Images via CNN

• Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things 

• 3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images

• Batch de-speckling of SAR images by a convolutional neural network-based method 

• Iterative PET Image Reconstruction Using Convolutional Neural Network Representation 

• Wildfire Segmentation on Satellite Images using Deep Learning

• Multi Modal Medical Image Fusion Based on Two Scale Image Decomposition and Spare Representation

• Multi-classification of Brain Tumor Images Using Deep Neural Network

• Convolutional Neural Network Based Image Distortion Classification

• Ship Detection from Optical Satellite Images with Deep Learning

• Blood Cell Image Classification

• Image Based Stress Detection Using Deep Learning

• Interval-Arithmetic Vector Quantization for Image Compression

• Stress Detection using Facial Image Data

• Water Level Prediction from Satellite Image

• A Supervised Segmentation Network for Hyperspectral Image Classification 

• Satellite Image Classification Method Using ELBP and SVM Classifier

• An X-ray Image Enhancement Algorithm for Dangerous Goods in Airport Security Inspection 

• Breast Cancer Classification using Capsule Network with Preprocessed Histology Images 

• Automatic Detection of Exudates in Digital Color Fundus Images Using Superpixel Multi-feature Classification

• Bacteria Classification using Image Processing and Deep learning

• Convolution Structure Sparse Coding for Fusion of Panchromatic and Multi-spectral Images 

Must Read: Image Processing Final Year Projects

Summary