Image denoising based on Laplace distribution with local parameters in Lapped Transform domain

In this paper, we present a new image denoising method based on statistical modeling of Lapped Transform (LT) coefficients. The lapped transform coefficients are first rearranged into wavelet like structure, then the rearranged coefficient subband statistics are modeled in a similar way like wavelet coefficients. We propose to model the rearranged LT coefficients in a subband using Laplace probability density function (pdf) with local variance. This simple distribution is well able to model the locality and the heavy tailed property of lapped transform coefficients. A maximum a posteriori (MAP) estimator using the Laplace probability density function (pdf) with local variance is used for the estimation of noise free lapped transform coefficients. Experimental results show that the proposed low complexity image denoising method outperforms several wavelet based image denoising techniques and also outperforms two existing LT based image denoising schemes. Our main contribution in this paper is to use the local Laplace prior for statistical modeling of LT coefficients and to use MAP estimation procedure with this proposed prior to restore the noisy image LT coefficients.