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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Image inpainting based on sparse representations with a perceptual metric
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| Title: | Image inpainting based on sparse representations with a perceptual metric |
| Authors: | Ogawa, Takahiro Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB |
| Issue Date: | 5-Dec-2013 |
| Publisher: | Springer |
| Journal Title: | EURASIP Journal on Advances in Signal Processing |
| Volume: | 2013 |
| Start Page: | 179 |
| Publisher DOI: | 10.1186/1687-6180-2013-179 |
| Abstract: | This paper presents an image inpainting method based on sparse representations optimized with respect to a perceptual metric. In the proposed method, the structural similarity (SSIM) index is utilized as a criterion to optimize the representation performance of image data. Specifically, the proposed method enables the formulation of two important procedures in the sparse representation problem, ‘estimation of sparse representation coefficients’ and ‘update of the dictionary’, based on the SSIM index. Then, using the generated dictionary, approximation of target patches including missing areas via the SSIM-based sparse representation becomes feasible. Consequently, image inpainting for which procedures are totally derived from the SSIM index is realized. Experimental results show that the proposed method enables successful inpainting of missing areas. |
| Rights: | http://creativecommons.org /licenses/by/2.0 |
| Type: | article |
| URI: | http://hdl.handle.net/2115/70669 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 小川 貴弘
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