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A cellular automata‐based filtering approach to multi‐temporal image denoising
Author(s) -
Priego Blanca,
Prieto Abraham,
Duro Richard J.,
Chanussot Jocelyn
Publication year - 2018
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12235
Subject(s) - computer science , cellular automaton , noise reduction , key (lock) , pixel , frame (networking) , artificial intelligence , adaptation (eye) , sequence (biology) , image (mathematics) , neighbourhood (mathematics) , set (abstract data type) , noise (video) , pattern recognition (psychology) , algorithm , mathematics , telecommunications , mathematical analysis , physics , computer security , biology , optics , genetics , programming language
Abstract This work addresses the problem of denoising image sequences through an approach that makes use of spatio‐temporal cellular automata‐based filtering. The algorithm is called st‐CAF and one of its key aspects is that the resulting cellular automata contemplate a spatio‐temporal neighbourhood when processing each pixel of the sequence. Additionally, the way the rule sets for the cellular automata are obtained, through evolutionary means, is also relevant, as it allows a good adaptation to any type of image and noise through the appropriate training set. This results in a great advantage over more traditional single frame denoising techniques presented in the literature or even over their adaptation to sequences. A fact that is made relevant in this paper through the application of the algorithm to different types of noisy images and its comparison to other techniques.