4DCAF: A temporal approach for denoising hyperspectral image sequences

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 denoising of (still) hyperspectral images, very little has been published on denoising hyperspectral sequences. This paper presents a novel denoising method for actual hyperspectral sequences. The approach is based on spatio-spectral-temporal cellular automata-based filtering. It presents several advantages, especially the fact that the cellular automaton used is able to contemplate information concerning the type of noise present through the use of specific sequences to tune the algorithm. It also considers temporal information by means of a spatio-temporal neighborhood when processing each pixel of the sequence. The proposed method outperforms several state-of-the-art algorithms on both simulated and real sequences.