Preprint Article Version 1 This version is not peer-reviewed

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Version 1 : Received: 12 January 2019 / Approved: 15 January 2019 / Online: 15 January 2019 (07:18:33 CET)

A peer-reviewed article of this Preprint also exists.

Abramov, S.; Uss, M.; Lukin, V.; Vozel, B.; Chehdi, K.; Egiazarian, K. Enhancement of Component Images of Multispectral Data by Denoising with Reference. Remote Sens. 2019, 11, 611. Abramov, S.; Uss, M.; Lukin, V.; Vozel, B.; Chehdi, K.; Egiazarian, K. Enhancement of Component Images of Multispectral Data by Denoising with Reference. Remote Sens. 2019, 11, 611.

Journal reference: Remote Sens. 2019, 11, 611
DOI: 10.3390/rs11060611

Abstract

Multispectral remote sensing data may contain component images which are heavily corrupted by noise and 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 which 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. Examples of denoising of real-life images demonstrate high efficiency of the proposed approach.

Subject Areas

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

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.