DocumentCode :
3515452
Title :
Adaptive reconstruction method of missing textures based on kernel canonical correlation analysis
Author :
Ogawa, Takahiro ; Haseyama, Miki
Author_Institution :
Grad. Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo
fYear :
2009
fDate :
19-24 April 2009
Firstpage :
1165
Lastpage :
1168
Abstract :
This paper presents an adaptive reconstruction method of missing textures based on kernel canonical correlation analysis (CCA). The proposed method calculates the correlation between two areas, which respectively correspond to a missing area and its neighbor area, from known parts within the target image and realizes the estimation of the missing textures. In order to obtain this correlation, the kernel CCA is applied to each set containing the same kind of textures, and the optimal result is selected for the target missing area. Specifically, a new approach monitoring errors caused in the above estimation process enables the selection of the optimal result. This approach provides a solution to the problem in traditional methods of not being able to perform adaptive reconstruction of the target textures due to the missing intensities. Experimental results show subjective and quantitative improvement of the proposed reconstruction technique over previously reported reconstruction techniques.
Keywords :
correlation methods; image reconstruction; image texture; adaptive reconstruction method; estimation process; kernel canonical correlation analysis; missing textures; Image reconstruction; Image restoration; Image texture analysis; Information analysis; Information science; Interpolation; Kernel; Monitoring; Pixel; Reconstruction algorithms; Image restoration; image texture analysis; interpolation; nonlinear estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
ISSN :
1520-6149
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2009.4959796
Filename :
4959796
Link To Document :
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