A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise
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Date
2020
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Publisher
Institute of Electrical and Electronics Engineers
Abstract
Remotely sensed images are widely used in many imaging applications. Images captured under adverse atmospheric conditions lead to degraded images that are contrast deficient and noisy. This study is intended to address these defects of remotely sensed data efficiently. A perceptually inspired variational model is designed based upon the Bayesian framework, powered by the retinex theory. The atmospheric noise or the shot noise (precisely following a Poisson distribution) and contrast inhomogeneity are addressed in this article. The model thus designed is tested and verified both visually and quantitatively using various test data under different statistical measures. The comparative study reveals the efficiency of the model. © 2020 IEEE.
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Keywords
Data visualization, Image reconstruction, Poisson distribution, Remote sensing, Shot noise, Atmospheric conditions, Contrast Enhancement, De-noising, Imaging applications, Perceptual image processing, Remotely sensed images, Statistical measures, Variational methods, Image enhancement, image analysis, image processing, remote sensing, satellite imagery
Citation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13, , pp. 941-949