Open AccessA multi-level method noise based image denoising using convolution neural network
Author(s) -
Alakananda Chakraborty,
Muskan Jindal,
Eshan Bajal,
Prabhishek Singh,
Manoj Diwakar,
Chandrakala Arya,
Amrendra Tripathi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1854/1/012040
Subject(s) - noise reduction , computer science , image denoising , artificial intelligence , leverage (statistics) , noise (video) , gaussian noise , convolutional neural network , convolution (computer science) , image (mathematics) , pattern recognition (psychology) , image restoration , artificial neural network , image processing
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.