SIMRES-TV: NOISE AND RESIDUAL SIMILARITY FOR PARAMETER ESTIMATION IN TOTAL VARIATION
Image restoration with regularization models is very popular in the image processing literature. Total variation (TV) is one of the important edge preserving regularization models used, however, to obtain optimal restoration results the regularization parameter needs to be set appropriately. We prop...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2021-04-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-2-W1-2021/171/2021/isprs-archives-XLIV-2-W1-2021-171-2021.pdf |
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| author | V. B. S. Prasath V. B. S. Prasath V. B. S. Prasath V. B. S. Prasath N. N. Hien D. N. H. Thanh S. Dvoenko |
| author_facet | V. B. S. Prasath V. B. S. Prasath V. B. S. Prasath V. B. S. Prasath N. N. Hien D. N. H. Thanh S. Dvoenko |
| author_sort | V. B. S. Prasath |
| collection | DOAJ |
| description | Image restoration with regularization models is very popular in the image processing literature. Total variation (TV) is one of the important edge preserving regularization models used, however, to obtain optimal restoration results the regularization parameter needs to be set appropriately. We propose here a new parameter estimation approach for total variation based image restoration. By utilizing known noise levels we compute the regularization parameter by reducing the similarity between residual and noise variances. We use the split Bregman algorithm for the total variation along with this automatic parameter estimation step to obtain a very fast restoration scheme. Experimental results indicate the proposed parameter estimation obtained better denoised images and videos in terms of PSNR and SSIM measures and the computational overload is less compared with other approaches. |
| first_indexed | 2024-12-17T20:26:29Z |
| format | Article |
| id | doaj.art-cbc673a8282047a28cfcb638d31058c0 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-17T20:26:29Z |
| publishDate | 2021-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-cbc673a8282047a28cfcb638d31058c02022-12-21T21:33:45ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342021-04-01XLIV-2-W1-202117117610.5194/isprs-archives-XLIV-2-W1-2021-171-2021SIMRES-TV: NOISE AND RESIDUAL SIMILARITY FOR PARAMETER ESTIMATION IN TOTAL VARIATIONV. B. S. Prasath0V. B. S. Prasath1V. B. S. Prasath2V. B. S. Prasath3N. N. Hien4D. N. H. Thanh5S. Dvoenko6Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229 USADepartment of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45257, USADepartment of Biomedical Informatics, College of Medicine, University of Cincinnati, OH 45267 USADepartment of Electrical Engineering and Computer Science, University of Cincinnati, OH 45221, USADong Thap University, Cao Lanh City, VietnamDepartment of Information Technology, School of Business Information Technology, University of Economics Ho Chi Minh City, VietnamInstitute of Applied Mathematics and Computer Science, Tula State University, RussiaImage restoration with regularization models is very popular in the image processing literature. Total variation (TV) is one of the important edge preserving regularization models used, however, to obtain optimal restoration results the regularization parameter needs to be set appropriately. We propose here a new parameter estimation approach for total variation based image restoration. By utilizing known noise levels we compute the regularization parameter by reducing the similarity between residual and noise variances. We use the split Bregman algorithm for the total variation along with this automatic parameter estimation step to obtain a very fast restoration scheme. Experimental results indicate the proposed parameter estimation obtained better denoised images and videos in terms of PSNR and SSIM measures and the computational overload is less compared with other approaches.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-2-W1-2021/171/2021/isprs-archives-XLIV-2-W1-2021-171-2021.pdf |
| spellingShingle | V. B. S. Prasath V. B. S. Prasath V. B. S. Prasath V. B. S. Prasath N. N. Hien D. N. H. Thanh S. Dvoenko SIMRES-TV: NOISE AND RESIDUAL SIMILARITY FOR PARAMETER ESTIMATION IN TOTAL VARIATION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | SIMRES-TV: NOISE AND RESIDUAL SIMILARITY FOR PARAMETER ESTIMATION IN TOTAL VARIATION |
| title_full | SIMRES-TV: NOISE AND RESIDUAL SIMILARITY FOR PARAMETER ESTIMATION IN TOTAL VARIATION |
| title_fullStr | SIMRES-TV: NOISE AND RESIDUAL SIMILARITY FOR PARAMETER ESTIMATION IN TOTAL VARIATION |
| title_full_unstemmed | SIMRES-TV: NOISE AND RESIDUAL SIMILARITY FOR PARAMETER ESTIMATION IN TOTAL VARIATION |
| title_short | SIMRES-TV: NOISE AND RESIDUAL SIMILARITY FOR PARAMETER ESTIMATION IN TOTAL VARIATION |
| title_sort | simres tv noise and residual similarity for parameter estimation in total variation |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-2-W1-2021/171/2021/isprs-archives-XLIV-2-W1-2021-171-2021.pdf |
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