DERMATOLOGICAL IMAGE DENOISING USING ADAPTIVE HENLM METHOD
In this paper we propose automatic image denoising method based on Hermite functions (HeNLM). It is an extension of non-local means (NLM) algorithm. Differences between small image blocks (patches) are replaced by differences between feature vectors thus reducing computational complexity. The featur...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-05-01
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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/XLII-2-W12/47/2019/isprs-archives-XLII-2-W12-47-2019.pdf |
| _version_ | 1818040611423387648 |
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| author | A. Dogvanich N. Mamaev A. Krylov N. Makhneva |
| author_facet | A. Dogvanich N. Mamaev A. Krylov N. Makhneva |
| author_sort | A. Dogvanich |
| collection | DOAJ |
| description | In this paper we propose automatic image denoising method based on Hermite functions (HeNLM). It is an extension of non-local means (NLM) algorithm. Differences between small image blocks (patches) are replaced by differences between feature vectors thus reducing computational complexity. The features are calculated in coordinate system connected with image gradient and are invariant to patch rotation. HeNLM method depends on the parameter that controls filtering strength. To chose automatically this parameter we use a no-reference denoising quality assessment method. It is based on Hessian matrix analysis. We compare the proposed method with full-reference methods using PSNR metrics, SSIM metrics, and its modifications MSSIM and CMSC. Image databases TID, DRIVE, BSD, and a set of dermatological immunofluorescence microscopy images were used for the tests. It was found that more perceptual CMSC and MSSIM metrics give worse correspondence than SSIM and PSNR to the results of information preservation by the non-reference image denoising. |
| first_indexed | 2024-12-10T08:17:17Z |
| format | Article |
| id | doaj.art-8312f239e83e4725b71a21631b367e8e |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-10T08:17:17Z |
| publishDate | 2019-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-8312f239e83e4725b71a21631b367e8e2022-12-22T01:56:26ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342019-05-01XLII-2-W12475210.5194/isprs-archives-XLII-2-W12-47-2019DERMATOLOGICAL IMAGE DENOISING USING ADAPTIVE HENLM METHODA. Dogvanich0N. Mamaev1A. Krylov2N. Makhneva3Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, 119991, Russia, Leninskie Gory, MSU BMK, RussiaFaculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, 119991, Russia, Leninskie Gory, MSU BMK, RussiaFaculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, 119991, Russia, Leninskie Gory, MSU BMK, RussiaMoscow Regional clinic of dermatology and venereology, Moscow, RussiaIn this paper we propose automatic image denoising method based on Hermite functions (HeNLM). It is an extension of non-local means (NLM) algorithm. Differences between small image blocks (patches) are replaced by differences between feature vectors thus reducing computational complexity. The features are calculated in coordinate system connected with image gradient and are invariant to patch rotation. HeNLM method depends on the parameter that controls filtering strength. To chose automatically this parameter we use a no-reference denoising quality assessment method. It is based on Hessian matrix analysis. We compare the proposed method with full-reference methods using PSNR metrics, SSIM metrics, and its modifications MSSIM and CMSC. Image databases TID, DRIVE, BSD, and a set of dermatological immunofluorescence microscopy images were used for the tests. It was found that more perceptual CMSC and MSSIM metrics give worse correspondence than SSIM and PSNR to the results of information preservation by the non-reference image denoising.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W12/47/2019/isprs-archives-XLII-2-W12-47-2019.pdf |
| spellingShingle | A. Dogvanich N. Mamaev A. Krylov N. Makhneva DERMATOLOGICAL IMAGE DENOISING USING ADAPTIVE HENLM METHOD The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | DERMATOLOGICAL IMAGE DENOISING USING ADAPTIVE HENLM METHOD |
| title_full | DERMATOLOGICAL IMAGE DENOISING USING ADAPTIVE HENLM METHOD |
| title_fullStr | DERMATOLOGICAL IMAGE DENOISING USING ADAPTIVE HENLM METHOD |
| title_full_unstemmed | DERMATOLOGICAL IMAGE DENOISING USING ADAPTIVE HENLM METHOD |
| title_short | DERMATOLOGICAL IMAGE DENOISING USING ADAPTIVE HENLM METHOD |
| title_sort | dermatological image denoising using adaptive henlm method |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W12/47/2019/isprs-archives-XLII-2-W12-47-2019.pdf |
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