Wavelet Based Image Denoising Using Adaptive Thresholding

The denoising of a natural image corrupted by Gaussian noise is a long established problem in signal or image processing. Even though much work has been done in the field of wavelet thresholding, most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds. This paper 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 result shows that the proposed denoising method outperforms standard wavelet denoising techniques in terms of the PSNR and the preservation of edge information. We have compared this with various denoising methods like Wiener filter, Visu shrink, Oracle shrink and Bayes shrink.