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An Analysis of Denoising Neural Networks for Noise Removal in Images

Publication Type : Journal Article

Publisher : Przeglad Elektrotechniczny

Source : Przeglad Elektrotechniczny 99, no. 2 (2023).

Url : https://pe.org.pl/articles/2023/2/4.pdf

Campus : Bengaluru

School : School of Engineering

Department : Electrical and Electronics

Year : 2023

Abstract : Clean images, when subjected to prolonged transmission, improper image acquisition or conditioned to multiple feature changes, lead to image tarnishing due to unwanted noisy pixels. This proposes to be a major threat in image-processing and computer vision fields. With the evolution of denoising models in the field of Neural Networks, efficient noise removal has become achievable, in a real-time scenario. In this work, two approaches to noise modelling have been considered, i.e., noise as an inverse problem and noise as a residual problem, this has been done by constructing convolutional auto encoders and denoising convolutional networks and their performance in the process of noise removal has been evaluated based on Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM).

Cite this Research Publication : Ramakotti, Raksha, Surekha Paneerselvam. "An Analysis of Denoising Neural Networks for Noise Removal in Images." Przeglad Elektrotechniczny, 99, no. 2 (2023).

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