Facial image noise classification and denoising using neural network

Milan Tripathi

doi:10.37868/sei.v3i2.id142

Abstract

Image denoising is an important aspect of image processing. Noisy images are produced as a result of technical and environmental flaws. As a result, it is reasonable to consider image denoising an important topic to research, as it also aids in the resolution of other image processing issues. The challenge, however, is that the traditional techniques used are time-consuming and inflexible. This article purposed a system of classifying and denoising noised images. A CNN and UNET based model architecture is designed, implement, and evaluated. The facial image dataset is processed and then it is used to train, valid and test the models. During preprocessing, the images are resized into 48*48, normalize, and various noises are added to the image. The preprocessing for each model is a bit different. The training and validation accuracy for the CNN model is 99.87% and 99.92% respectively. The UNET model is also able to get optimal PSNR and SSIM values for different noises.

Highlights

Image Denoising is a crucial topic in image processing and a lot of work is currently being done on it, but there is very little attention towards automating the task of classifying the noised image
Few researchers have work in this field and many papers still only focus on the latter part of denoising
Image denoising is a crucial task in image processing and deep learning [11,12,13,14,15]

Summary

Introduction

Image Denoising is a crucial topic in image processing and a lot of work is currently being done on it, but there is very little attention towards automating the task of classifying the noised image. Few researchers have work in this field and many papers still only focus on the latter part of denoising. I want to design a convolutional neural network that classifies the noised images into different classes and a UNET based model for denoising the noised image. Since the manual selection of images consumes huge time, automatic classification and denoising save a lot of time and effort

Literature review

Deep learning model

Results

Evaluation of the trained model

Conclusion