Abstract
A new natural image denoising method using a modified sparse coding (SC) algorithm proposed by us was discussed in this paper. 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 as the initialization feature basis function of our sparse coding algorithm instead of using a random initialization matrix. This denoising method is evaluated by values of the normalized mean squared error (NMSE) and signal to noise ratio (NSNR). Compared with other denoising methods, the simulation results show that our SC shrinkage technique is indeed effective.
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Shang, L., Cao, F., Chen, J. (2008). Denoising Natural Images Using Sparse Coding Algorithm Based on the Kurtosis Measurement. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_40
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DOI: https://doi.org/10.1007/978-3-540-87734-9_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87733-2
Online ISBN: 978-3-540-87734-9
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