Nakagaki, R.
Katsaggelos, A.K.
Production Eng. Res. Lab., Hitachi Ltd., Yokohama, Japan
This paper appears in: Image Processing, IEEE Transactions on Publication Date: Sept. 2003
Volume: 12
,
Issue: 9
On page(s):
1044
- 1053
ISSN: 1057-7149
Digital Object Identifier: 10.1109/TIP.2003.816007
Current Version Published: 2003-08-18
Abstract
Learning-based algorithms for imagerestoration and blind imagerestoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.
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