Wavelet-Based CR Image Denoising by Exploiting Inner-Scale Dependency

Abstract

Filtering is a preliminary process in many medical image processing applications. It is aiming at reducing noise in images, and any post-processing tasks may benefit from the reduction of noise. The major two noises in computed radiography (CR) images are Gaussian white noise and Poisson noise. By considering both the characteristics of CR images and the statistical features of wavelet transformed coefficients, an efficient spatial adaptive filtering algorithm, which is based on statistical model of local dependency of CR image wavelet coefficients and the approximate minimum mean squared error (MMSE) estimation, is proposed to decrease the Gaussian white noise in computed images. The process is computational cost saving, and the denoising experiments show the algorithm outperforms other approaches in peak-signal-to-noise ratio (PSNR).