An adaptive spatial–spectral total variation approach for Poisson noise removal in hyperspectral images. Issue 3 (March 2016)

• Record Type: Journal Article
• Authors: Mansouri, Alamin; Deger, Ferdinand; Pedersen, Marius; Hardeberg, Jon; Voisin, Yvon
• Is Part Of: Signal, image and video processing. Volume 10:Issue 3(2016)
• Page Start: 447
• Page End: 454
• Publication Date: 2016-03
• Subjects: Adaptive total variation -- Hyperspectral images -- Poisson noise; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-015-0806-0
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract Poisson distributed noise, such as photon noise, is an important noise source in multi- and hyperspectral images. We propose a variational-based denoising approach that accounts the vectorial structure of a spectral image cube, as well as the Poisson distributed noise. For this aim, we extend an approach initially developed for monochromatic images, by a regularisation term, which is spectrally and spatially adaptive and preserves edges. In order to take the high computational complexity into account, we derive a split Bregman optimisation for the proposed model. The results show the advantages of the proposed approach compared with a marginal approach on synthetic and real data.