Improved Algorithm for Non-local PC A Poisson Noise Image Restoration Based on L1/2 Norms
In order to mitigate the issue of image blurring during restoration by using the original NLSPCA(Non-Local Sparse Principal Component Analysis),we propose a novel non-local PCA Pois-son noise image restoration algorithm based on L1/2 norms(L1/2-NLSPCA)to improve enhance the performance in removing Poisson noise from images.Firstly,the proposed method segments the image into overlapping blocks;secondly,the designed adaptive Bregman K-means algorithm clusters the seg-mented image blocks to improve the accuracy of image block clustering;finally,we utilize PCA to construct a non-local dictionary and obtain sparse representation coefficients based on L1/2 norms,which are subsequently employed in the denoising and reconstruction of the clustered image blocks.L1/2 norms can increase the sparsity of the representation coefficients under the dictionary more effi-ciently.Experimental results show that the L1/2-NLSPCA algorithm improves the peak signal-to-noise ratio(PSNR)by 0.52 to 2.57 dB compared to with the benchmark algorithm,and the texture details are clearer visually visually clearer.
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