Image enhancement via MMSE estimation of Gaussian scale mixture with Maxwell density in AWGN

Abstract

In optical techniques, noise signal is a classical problem in medical image processing. Recently, there has been considerable interest in using the wavelet transform with Bayesian estimation as a powerful tool for recovering image from noisy data. In wavelet domain, if Bayesian estimator is used for denoising problem, the solution requires a prior knowledge about the distribution of wavelet coe±cients. Indeed, wavelet coe±cients might be better modeled by super Gaussian density. The super Gaussian density can be generated by Gaussian scale mixture (GSM). So, we present new minimum mean square error (MMSE) estimator for spherically-contoured GSM with Maxwell distribution in additive white Gaussian noise (AWGN). We compare our proposed method to current state-of-the-art method applied on standard test image and we quantify achieved performance improvement.

Image quality improvement using dddtdwt

“…The images are degraded during transmission. The reason for degradation are noises from the channel, instruments and surrounding issues [1][2]. The researchers have used different method to enhance the degraded images.…”