Immune-clonal-selection-based nonsubsampled contourlet domain image denoising method



The invention relates to the application of an image processing technology in the field of image denoising, in particular to an immune-clonal-selection-based nonsubsampled contourlet domain image denoising method, which is characterized by comprising the following steps of: (1) inputting a noisy image X, and performing L-layer nonsubsampled contourlet domain decomposition on the noisy image X to obtain high-frequency directional sub-bands {Dl,i(m,n),} in different scales and a low-frequency sub-band AL(m,n), wherein l is more than or equal to 1 and less than or equal to L-1, i is more than or equal to 1 and less than or equal to kl, kl is the number of the high-frequency direction sub-bands in the scale of 2-l, Dl,i(m,n) represents a coefficient at a pixel position (m,n) on the ith high-frequency directional sub-band of the noisy image in the scale of 2-l, and L is 3 to 5; and (2) searching for an optimal denoising threshold value {Tl,i} of each high-frequency directional sub-band in the different scales by using an immune clonal selection method, wherein l is more than or equal to 0 and less than or equal to L-1, and i is more than or equal to 1 and less than or equal to kl. Compared with the prior art, the invention searches for denoising threshold values by adopting the immune clonal selection method without knowing the exact characteristics of image noise, so that better denoising threshold values can be found; and due to the adoption of nonsubsampled contourlet transform, jitter distortion caused by the deficiency of translation invariance of a transform tool can be effectively avoided.




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