Open AccessPerformance Analysis of Image Denoising using Deep Convolutional Neural Network
Author(s) -
S. Thayammal,
G. SankaraMalliga,
Subhashree Priyadarsini,
K. Ramalakshmi
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1070/1/012085
Subject(s) - convolutional neural network , artificial intelligence , computer science , noise reduction , image (mathematics) , noise (video) , image quality , image denoising , computer vision , pattern recognition (psychology) , non local means , enhanced data rates for gsm evolution , artificial neural network
A performance analysis of conventional Convolutional Neural Network (CNN) based denoising method is proposed. In this image denoising method, the contrast of images is adaptively enhanced. Generally, it is not possible to capture the imageswith good quality for all situations. Because they are captured in various light conditions.So, the captured images are suffered by noise, which results in poor perceived image quality. Thus, it is necessary to improve the quality of images with edge detail preservation as much as possible. The convolutional neural network model for low light image enhancement is already developed and is named as DnCNNs. Here, the performance analysis of image denoising using the DnCNNmodel is presented. The DnCNN implicitly removes the noise in the image. The simulation results afford better reference for application developers.