Digital image processing: concepts, algorithms and scientific applications
Digital image processing: concepts, algorithms and scientific applications
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Handbook of Image and Video Processing
Handbook of Image and Video Processing
Palmprint recognition using 2d-gabor wavelet based sparse coding and RBPNN classifier
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
MMW image reconstruction combined NNSC shrinkage technique and PDEs algorithm
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
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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.