Image denoising using local contrast and adaptive mean in wavelet transform domain

Images are often corrupted by noise due to the imperfection of image acquisition systems and transmission channels. Traditional algorithms perform image denoising in the pixel domain. However, the transform domain denoising methods have shown outstanding success over the last decades. There are many image denoising methods which are in existence over the last decades, originated from various disciplines such as probability theory, statistics, partial differential equations, linear and nonlinear filtering, spectral and multiresolution analysis due to the robustness of the systems. Recently, image denoising has been attracting much attention using the wavelet transform. Wavelet based approach provides a particularly useful method for image denoising when the preservation of contents in the scene is of importance because the local adaptivity is based explicitly on the values of the wavelet detail coefficients. In this paper, we have proposed a new thresholding technique based on local contrast and adaptive mean in the wavelet transform domain.