This study proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding based on the Generalized Gaussian Distribution (GGD) widely used in image processing applications. The proposed threshold is simple and it is adaptive to each sub band because it depends on data-driven estimates of the parameters. In this proposed method, the choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet sub band coefficients like standard deviation, variance. Our method describes a new method for suppression of noise in image by fusing the wavelet denoising technique with optimized thresholding function improving the denoised results significantly. Simulated noise images are used to evaluate the denoising performance of proposed algorithm along with another wavelet-based denoising algorithm. Experimental results show that the proposed denoising method outperforms standard wavelet denoising techniques in terms of the PSNR and the prevented edge information in most cases. We have compared this with various denoising methods like wiener filter, VisuShrink and BayesShrink.
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