Image de-noising with an optimal threshold using wavelets

Image de-noising is a problem of prime importance in image processing field,ranging from medical imaging to satellite imaging.Images are often corrupted by additive noise that can be modeled as Gaussian most of the time.The main purpose of an image de-noising algorithm is to reduce the noise level,while preserving the image features.In wavelet domain soft or hard thresholding is used for de-noising purpose.In this paper We propose new method to determine an optimal threshold using neighborhood window coefficients.The results of the proposed method are compared with BayesShrink, VisuShrin and SureShrink, using a mean squared error criterion and peak signal to noise ratio. The results show that the proposed technique yield improved performance.