Abstract
Image denoising is a noise removal technique used to remove noise from noisy image. The wavelet is one of the most popular techniques in recent developments in image denoising. It is effective in denoising because of its energy transformation ability to get wavelet coefficients. It is not possible to get noise suppression and characteristics preservation of the image at the same time. In this paper an improved method is presented by which the optimal threshold for every sub-band in neighboring window is determined by Stein’s Unbiased Risk Estimator (SURE). Then, the neigh shrink is applied in the neighboring window to get optimal PSNR (Peak Signal to Noise Ratio). The main aim of this research work is to increase the PSNR of an image while keeping the Mean Square Error (MSE) low. The algorithm was tested on various images and the results for different PSNR and MSE values are presented in this research paper.
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