A cellular automata‐based filtering approach to multi‐temporal image denoising. Issue 2 (6th September 2017)

• Record Type: Journal Article
• Authors: Priego, Blanca; Prieto, Abraham; Duro, Richard J.; Chanussot, Jocelyn
• Other Names: GarcÍa‐RodrÍguez José guestEditor.; Escalera Sergio guestEditor.; Psarrou Alexandra guestEditor.; Guyon Isabelle guestEditor.; Lewis Andrew guestEditor.; Leitner Juxi guestEditor.
• Is Part Of: Expert systems. Volume 35:Issue 2(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-09-06
• Subjects: cellular automata -- low‐dose X‐ray image -- signal‐dependent noise -- spatio‐temporal denoising; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12235
• Languages: English
• ISSNs: 0266-4720

Abstract

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.