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Denoising natural images based on a modified sparse coding algorithm
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AI
DOI：10.1016/j.amc.2008.05.018.png)
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This paper proposes a novel image reconstruction method for natural images using a modified sparse coding (SC) algorithm proposed by us. This SC algorithm exploited the maximum Kurtosis as the maximizing sparse measure criterion at one time, a fixed variance term of sparse coefficients is used to yield a fixed information capacity. On the other hand, in order to improve the convergence speed, we use a determinative basis function, which is obtained by a fast fixed-point independent component analysis (FastICA) algorithm, as the initialization feature basis function of our sparse coding algorithm instead of using a random initialization matrix. The experimental results show that by using our SC algorithm, the feature basis vectors of natural images can be successfully extracted. Then, exploiting these features, the original images can be reconstructed easily. Furthermore, compared with the standard ICA method, the experimental results show that our SC algorithm is indeed efficient and effective in performing image reconstruction task. (C) 2008 Elsevier Inc. All rights reserved.
Keyword:
Sparse coding
Kurtosis
Fixed variance
Image feature extraction
Image reconstruction
期刊
IF:
3.4
论文数:
2.3W
被引数:
3.3W
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