A modified image denoising algorithm by labeling and 3D wavelet transform
Shunyong Zhou, Xingzho Xiong, Wenling Xie · 2010
In order to sharpen image details and reducing noise, based on the multi-analysis wavelet threshold denoising method, a Labeling-based block-matching and wavelet transform filtering method combine hard and soft threshold denoising approaches (BWHS) is proposed in this paper. First, we estimate the noise variance of image. Second compute the matching blocks, and construct the 3D data array of those similar blocks, the high and low frequency sub-bands denoised by the best soft threshold, hard threshold that result from the iterative calculation of noise variance respectively, Finally, sharpen image details using DC coefficients of LL frequency sub-bands. Simulation results show that the algorithm can preserve and sharpen image details and effectively attenuate noise. Moreover, it has better performance than the traditional soft threshold, hard threshold, median and mean denoising methods.