Blind estimation of noise level based on pixels values prediction

Mykola Ponomarenko, Oleksandr Miroshnichenko, Vladimir Lukin, Karen Egiazarian

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Noise parameters estimation is required in various stages of digital image processing. Many efficient algorithms of noise estimation were proposed during last two decades. However, most of these algorithms are efficient only for a specific type of noise for which they are designed. For example, methods of variance estimation of additive white Gaussian noise (AWGN) will not work in the case of additive colored Gaussian noise (ACGN) or, in general, in the case of a noise with non AWGN distribution. In this paper, a totally blind method of noise level estimation is proposed. For a given image, a distorted image with a discarded portion of pixels (around 10%) is generated. Then an inpainting (or impulse noise removal) method is applied to recover those discarded pixels values. The difference between the true and recovered pixel values is used to robustly estimate image noise level. The algorithm is applied for different image scales to estimate a noise spectrum. In this paper, we propose a convolutional neural network called PIXPNet for effective prediction of values of missing pixels. A comparative analysis confirms that the proposed PIXPNet provides smallest error of recovered pixel values among all existing methods. A good efficiency of application of the proposed method in both AWGN and spatially correlated noise suppression is demonstrated.

Original languageEnglish
Title of host publicationProc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging, 2022
Number of pages5
Volume34
Edition14
DOIs
Publication statusPublished - 2022
Publication typeA4 Article in conference proceedings
EventIS and T International Symposium on Electronic Imaging: Computational Imaging -
Duration: 17 Jan 202226 Jan 2022

Publication series

NameIS and T International Symposium on Electronic Imaging Science and Technology
ISSN (Print)2470-1173

Conference

ConferenceIS and T International Symposium on Electronic Imaging: Computational Imaging
Period17/01/2226/01/22

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics

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