Enhancement of Component Images of Multispectral Data by Denoising with Reference

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 6; p. 611
• Main Authors: Abramov, Sergey, Uss, Mikhail, Lukin, Vladimir, Vozel, Benoit, Chehdi, Kacem, Egiazarian, Karen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2019; MDPI
• Subjects: BM3D-filtering; Collaboration; DCT-filtering; Engineering Sciences; filtering with reference; Image enhancement; Image filters; Image quality; multispectral imaging; Noise; Noise reduction; Parameter estimation; Principal components analysis; Remote sensing; Sensors; Signal and Image processing; Signal processing; vectorial (three-dimensional) filtering; France; United States > US; multispectral imaging; vectorial (three-dimensional) filtering; remote sensing; BM3D-filtering; DCT-filtering; filtering with reference
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11060611

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.