Enhancement of Component Images of Multispectral Data by Denoising with Reference

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 sim...

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Published inRemote 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
LanguageEnglish
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
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Abstract 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.
AbstractList 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
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.
Author Abramov, Sergey
Egiazarian, Karen
Lukin, Vladimir
Chehdi, Kacem
Vozel, Benoit
Uss, Mikhail
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  surname: Chehdi
  fullname: Chehdi, Kacem
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  surname: Egiazarian
  fullname: Egiazarian, Karen
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CitedBy_id crossref_primary_10_1080_13682199_2024_2449273
crossref_primary_10_1007_s11220_024_00504_2
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Issue 6
Keywords multispectral imaging
vectorial (three-dimensional) filtering
remote sensing
BM3D-filtering
DCT-filtering
filtering with reference
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SubjectTerms 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
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Title Enhancement of Component Images of Multispectral Data by Denoising with Reference
URI https://www.proquest.com/docview/2304012658
https://univ-rennes.hal.science/hal-02135627
https://doaj.org/article/ff2cb2f0ed2341e0b4194fad8c48a364
Volume 11
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