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ICIP
2007
IEEE

A VQ-Based Demosaicing by Self-Similarity

14 years 5 months ago
A VQ-Based Demosaicing by Self-Similarity
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.
Yoshikuni Nomura, Shree K. Nayar
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICIP
Authors Yoshikuni Nomura, Shree K. Nayar
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