TAILIEUCHUNG - Báo cáo hóa học: " Research Article Missing Texture Reconstruction Method Based on Perceptually Optimized Algorithm"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Missing Texture Reconstruction Method Based on Perceptually Optimized Algorithm | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 208976 13 pages doi 2010 208976 Research Article Missing Texture Reconstruction Method Based on Perceptually Optimized Algorithm Takahiro Ogawa and Miki Haseyama Graduate School of Information Science and Technology Hokkaido University Sapporo 060-0814 Japan Correspondence should be addressed to Takahiro Ogawa ogawa@ Received 23 August 2010 Revised 12 October 2010 Accepted 26 October 2010 Academic Editor Enrico Capobianco Copyright 2010 T. Ogawa and M. Haseyama. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. This paper presents a simple and effective missing texture reconstruction method based on a perceptually optimized algorithm. The proposed method utilizes the structural similarity SSIM index as a new visual quality measure for reconstructing missing areas. Furthermore in order to adaptively reconstruct target images containing several kinds of textures the following two novel approaches are introduced into the SSIM-based reconstruction algorithm. First the proposed method performs SSIM-based selection of the optimal known local textures to adaptively obtain subspaces for reconstructing missing textures. Secondly missing texture reconstruction that maximizes the SSIM index in the known neighboring areas is performed. In this approach the nonconvex maximization problem is reformulated as a quasi convex problem and adaptive reconstruction of the missing textures based on the perceptually optimized algorithm becomes feasible. Experimental results show impressive improvements of the proposed method over previously reported reconstruction methods. 1. Introduction Restoration of missing areas in digital images has been intensively studied since it can be applied to a number of

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