Abstract：Aiming at the problem of shortage of signal utilization by traditional K-SVD. Using sparse Bayesian learning to preprocess the image signal. Combining the orthogonal matching pursuit algorithm with the improved steepest descent algorithm. Taking into account the noise atoms are present in the dictionary after the update of the dictionary, so combined with the Bartlett test method to cut off the noise atoms. Experimental results show that the method is better than the wavelet threshold denoising algorithm and the sparse representation based on DCT dictionary. Also, the method can remove the noise better, preserve image edge information, obtain higher peak signal to noise ratio, and the resulting image has a better visual effect.
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2018, Vol. 39 
