Robust Statistical Enhancement Techniques for High-Density Impulse Noise Reduction

DOI

10.2991/978-94-6463-529-4_2How to use a DOI?

Keywords

High-Density Noise Removal; Statistical Enhancement Techniques; Salt and Pepper Noise

Abstract

Image quality enhancement via impulse noise reduction is a critical phase in image preprocessing. Faults in the acquisition, storage, and transmission devices often corrupt the images by introducing noise that further hinders image analysis and processing tasks. This paper focuses on high-density salt and pepper noise removal from images using statistical image enhancement techniques. We present two enhancement algorithms targeted at noise removal achieved through pixel regeneration. The first approach uses two-stage filtration based on an adaptive substructure; the noise is primarily eliminated using the non-noisy neighbors in an adaptive window, followed by fine-tuning the pixel intensity to remove artifacts. The second approach uses a quasi-adaptive substructure where the neighbors in primary directions contribute to the decision-making process of pixel regeneration based on their information relevance. Performance evaluation based on the inferences made from different experiments on multiple images verifies the efficiency of the presented techniques. The observed reliability and robustness reflected in the results suggest the superiority of the algorithms over their existing peers.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Arinjay Bhowmick
AU  - Rudrajit Choudhuri
AU  - Amiya Halder
PY  - 2024
DA  - 2024/10/04
TI  - Robust Statistical Enhancement Techniques for High-Density Impulse Noise Reduction
BT  - Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
PB  - Atlantis Press
SP  - 7
EP  - 21
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-529-4_2
DO  - 10.2991/978-94-6463-529-4_2
ID  - Bhowmick2024
ER  -