Title :
Adaptive reconstruction method of missing textures based on perceptually optimized algorithm
Author :
Ogawa, Takahiro ; Haseyama, Miki
Author_Institution :
Grad. Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo, Japan
Abstract :
This paper presents an adaptive reconstruction method of missing textures based on structural similarity (SSIM) index. The proposed method firstly performs SSIM-based selection of the optimal known local textures to adaptively obtain subspaces for reconstructing missing textures. Furthermore, from the selected known textures, the missing texture reconstruction maximizing the SSIM index is performed. In this approach, the non-convex maximization problem is reformulated as a quasi convex problem, and the adaptive reconstruction of the missing textures becomes feasible. Experimental results show impressive improvement of the proposed method over previously reported reconstruction methods.
Keywords :
concave programming; image reconstruction; image texture; adaptive reconstruction method; local textures; missing textures; nonconvex maximization problem; perceptually optimized algorithm; quasiconvex problem; structural similarity index; Equations; Image reconstruction; Image restoration; Indexes; Mathematical model; Pixel; Reconstruction algorithms; Image restoration; image quality assessment; image texture analysis; interpolation;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2011.5946614