Enhancement of Component Images of Multispectral Data by Denoising with Reference

Sergey Abramov (1) , Mikhail Uss (1) , Vladimir Lukin (1) , Benoit Vozel (2) , Kacem Chehdi (2) , Karen Egiazarian (3)

1 Kharkov National University
2 IETR - Institut d'Électronique et des Technologies du numéRique
3 Department of Signal Processing [Tampere]

Multispectral remote sensing data may contain component images that are heavily corrupted by noise and the pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images—component images having relatively high quality and that are similar to the image subject to pre-filtering. Here, we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. To provide high efficiency of denoising, reference images have to be transformed using linear or nonlinear transformations. This paper proposes a practical approach to doing this. Examples of denoising tests and real-life images demonstrate high efficiency of the proposed approach

Keywords: remote sensing; multispectral imaging; DCT-filtering; vectorial (three-dimensional) filtering; BM3D-filtering; filtering with reference

Domains: Engineering Sciences [physics]; Signal and Image processing

Sergey Abramov, Mikhail Uss, Vladimir Lukin, Benoit Vozel, Kacem Chehdi, et al.. Enhancement of Component Images of Multispectral Data by Denoising with Reference. Remote Sensing, 2019, 11 (6), pp.611. ⟨10.3390/rs11060611⟩. ⟨hal-02135627⟩