Unraveling the Power of Deep Learning in Image Processing

Table of Contents

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.

Must Read: Image Processing Final Year Projects

Summary

Deep learning projects have completely revolutionized the sector of image processing with the optimal usage of computers and advanced approaches like CNNs. The final year students can consider advanced deep learning projects if they prefer image processing domain.
Final year projects