DocumentCode :
2036934
Title :
A VQ-Based Demosaicing by Self-Similarity
Author :
Nomura, Yoshikuni ; Nayar, Shree K.
Author_Institution :
Sony Corp., Tokyo
Volume :
3
fYear :
2007
fDate :
Sept. 16 2007-Oct. 19 2007
Abstract :
In this paper, we propose a learning-based demosaicing and a restoration error detection. A Vector Quantization (VQ)-based method is utilized for learning. We take advantage of a self-similarity in an image for a codebook generation in VQ. The mosaic image is interpolated via a traditional method, and applied scaling, blurring, phase-shifting and resampling are used to create a training data for the codebook. The characteristics of the training data are similar to those of an ideal image. Using such training data and approximation of an ideal codevector by a locally linear embedding (LLE)-based method increases the probability of finding a suitable codevector from the codebook. Even if we cannot find a good codevector in an ill-conditioned case, the error detection finds poorly estimated pixel values and replaces them with better restoration results by another demosaicing method.
Keywords :
approximation theory; error detection; fractals; image coding; image restoration; image sampling; image segmentation; interpolation; learning (artificial intelligence); vector quantisation; VQ-based demosaicing; codebook generation; codevector approximation; image blurring; image resampling; interpolation; learning-based demosaicing; locally linear embeddingmethod; phase-shifting; restoration error detection; self-similarity; training data; vector quantization; Frequency; Image coding; Image reconstruction; Image resolution; Image restoration; Learning systems; Optical filters; Strontium; Training data; Vector quantization; Image reconstruction; Image resolution; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2007. ICIP 2007. IEEE International Conference on
Conference_Location :
San Antonio, TX
ISSN :
1522-4880
Print_ISBN :
978-1-4244-1437-6
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2007.4379345
Filename :
4379345
Link To Document :
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