A multi-level method noise based image denoising using convolution neural network

Author:

Chakraborty Alakananda,Jindal Muskan,Bajal Eshan,Singh Prabhishek,Diwakar Manoj,Arya Chandrakala,Tripathi Amrendra

Abstract

Abstract Gaussian noise has been the bane of any and every denoising process under the sun. Being a very corrosive noise with huge disruptive potential, this has received much attention form the image restoration community. Building on the premise, a novel framework is proposed to leverage multi-level image denoising that iteratively removes gaussian noise while recovering details lost during processing. This framework uses existing deep learning based CNN systems whilst enhancing the same by the addition of method denoising to the process. This framework is habile in competing with state-of-the-art technologies and outperforming them in some cases.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Reference29 articles.

1. Brief review of image denoising techniques;Fan;Visual Computing for Industry, Biomedicine, and Art,2019

2. A non-local algorithm for image denoising;Buades,2005

3. Medical image denoising using convolutional denoising autoencoders;Gondara,2016

4. Block-matching convolutional neural network for image denoising;Ahn,2017

5. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising;Zhang;IEEE Transactions on Image Processing,2017

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