| Paper: | TA-P6.8 |
| Session: | Image Scanning, Display, Printing, Color and Multispectral Processing II |
| Time: | Tuesday, September 18, 09:50 - 12:30 |
| Presentation: |
Poster
|
| Title: |
A VQ-BASED DEMOSAICING BY SELF-SIMILARITY |
| Authors: |
Yoshikuni Nomura; Sony Corporation | | |
| | Shree Nayar; Columbia University | | |
| 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. |
