A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise

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dc.contributor.authorFebin, I.P.
dc.contributor.authorPadikkal, P.
dc.contributor.authorBini, A.A.
dc.date.accessioned2026-02-05T09:29:10Z
dc.date.issued2020
dc.description.abstractRemotely 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.
dc.identifier.citationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13, , pp. 941-949
dc.identifier.issn19391404
dc.identifier.urihttps://doi.org/10.1109/JSTARS.2020.2975044
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/24178
dc.publisherInstitute of Electrical and Electronics Engineers
dc.subjectData visualization
dc.subjectImage reconstruction
dc.subjectPoisson distribution
dc.subjectRemote sensing
dc.subjectShot noise
dc.subjectAtmospheric conditions
dc.subjectContrast Enhancement
dc.subjectDe-noising
dc.subjectImaging applications
dc.subjectPerceptual image processing
dc.subjectRemotely sensed images
dc.subjectStatistical measures
dc.subjectVariational methods
dc.subjectImage enhancement
dc.subjectimage analysis
dc.subjectimage processing
dc.subjectremote sensing
dc.subjectsatellite imagery
dc.titleA Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise

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