A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise
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 variat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9016022/ |
| _version_ | 1818603587788341248 |
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| author | I. P. Febin P. Jidesh A. A. Bini |
| author_facet | I. P. Febin P. Jidesh A. A. Bini |
| author_sort | I. P. Febin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T13:25:33Z |
| format | Article |
| id | doaj.art-dd93f89f22ed40aeb78c61a6a957b23e |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-16T13:25:33Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-dd93f89f22ed40aeb78c61a6a957b23e2022-12-21T22:30:13ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352020-01-011394194910.1109/JSTARS.2020.29750449016022A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot NoiseI. P. Febin0P. Jidesh1https://orcid.org/0000-0001-9448-1906A. A. Bini2Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka, Surathkal, IndiaDepartment of Mathematical and Computational Sciences, National Institute of Technology Karnataka, Surathkal, IndiaIndian Institute of Information Technology, Kottayam, IndiaRemotely 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.https://ieeexplore.ieee.org/document/9016022/Contrast enhancementdenoisingperceptual image processingvariational method |
| spellingShingle | I. P. Febin P. Jidesh A. A. Bini A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Contrast enhancement denoising perceptual image processing variational method |
| title | A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise |
| title_full | A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise |
| title_fullStr | A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise |
| title_full_unstemmed | A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise |
| title_short | A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise |
| title_sort | retinex based variational model for enhancement and restoration of low contrast remote sensed images corrupted by shot noise |
| topic | Contrast enhancement denoising perceptual image processing variational method |
| url | https://ieeexplore.ieee.org/document/9016022/ |
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