A Perceptually Inspired Variational Model for Enhancing and Restoring Remote Sensing Images

Perceptually inspired algorithms have captured the recent attention of scientists and engineers due to their inherent capability to enhance the contrast of images, especially from the remote sensing domain. In this letter, we propose a perceptually inspired retinex model relying on the variational f...

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Veröffentlicht in:IEEE geoscience and remote sensing letters Jg. 18; H. 2; S. 251 - 255
Hauptverfasser: Jidesh, P., Febin, I. P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptation models
Algorithms
Contrast enhancement
Data models
denoising
Image contrast
Image enhancement
Image restoration
Noise reduction
Optimization
perceptual image processing
Remote sensing
Satellite imagery
Spaceborne remote sensing
variational method
Visualization
ISSN:1545-598X, 1558-0571
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Zusammenfassung:Perceptually inspired algorithms have captured the recent attention of scientists and engineers due to their inherent capability to enhance the contrast of images, especially from the remote sensing domain. In this letter, we propose a perceptually inspired retinex model relying on the variational framework for enhancing and denoising satellite images captured by various imaging devices. A variational framework incorporates priors and data fidelity aspects in the designed functional, whose optimized solution yields the desired output. The model respects the distribution of the noise while enhancing the data. The overall performance is demonstrated using the visual and quantitative measures.
Bibliographie:ObjectType-Article-1
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2020.2969411