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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...
As a consequence of the fast development of sensor technology in the last decade, it is now possible to acquire sequences of hyperspectral images at reasonable frame rates. However, these sequences may be significantly corrupted by noise, especially when the spectral coverage of the data reaches the thermal domain. While there is an abundant literature on denoising of (standard) video sequences or...
This work describes a novel spatio-temporal cellular automata-based filtering algorithm (st-CAF) intended for performing image sequence denoising processes. The approach presents several advantages over more traditional single frame denoising techniques presented in the literature or even over their adaptation to sequences. Especially the fact that the cellular automaton used is able to contemplate...
This work presents a novel spatio-temporal cellular automata-based filtering (STCAF) for image sequence denoising. Most of the methods using cellular automata (CA) for image denoising involve the manual design of the rules that define the behaviour of the automata. This is a complex and not straightforward operation. In order to tackle this problem, this paper proposes to use evolutionary methods...
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