Improvement of Image Denoising Algorithms by Preserving the Edges

Pandey, RK and Singh, H and Ramakrishnan, AG (2019) Improvement of Image Denoising Algorithms by Preserving the Edges. In: 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019, 3 - 5 September 2019, Salerno, pp. 496-506.

PDF CAIP_2019.pdf - Published Version Restricted to Registered users only Download (3MB) | Request a copy

Abstract

Image restoration is one of the well-studied problems in low-level image processing tasks. Recently, deep learning based image restoration techniques have shown promising results and outperform most of the state of the art image denoising algorithms. Most of the deep learning based methods use mean square error as a loss function to obtain the denoised output. This work focuses on further improving the existing deep learning based image denoising techniques by preserving edges using Canny edge based loss function, and hence improving peak signal to noise ratio (PSNR) and structural similarity (SSIM) of the images while restoring the visual quality.

Item Type:	Conference Paper
Publication:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher:	Springer Verlag
Additional Information:	The copyright for this article belongs to Springer Verlag.
Keywords:	Deep learning; Edge detection; Errors; Image analysis; Image enhancement; Image reconstruction; Mean square error; Restoration; Signal to noise ratio, De-noising; Edge preservations; Loss functions; Mean square; PSNR; SSIM, Image denoising
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	06 Dec 2022 07:10
Last Modified:	06 Dec 2022 07:10
URI:	https://eprints.iisc.ac.in/id/eprint/78275

Actions (login required)

View Item