Bayesian image denoising using two complementary discontinuity measures

TL;DR: In this article, the authors proposed a Bayesian denoising framework using two complementary discontinuity measures, namely local-inhomogeneity and spatial-gradient, to detect significant discontinuities from noisy images.

Abstract: This study introduces a novel Bayesian image denoising method using two complementary discontinuity measures. The first discontinuity measure is the spatial-gradient, which has been widely used as a discontinuity measure. Although the spatial-gradient measure effectively preserves edge components in images, it is inadequate to detect significant discontinuities from noisy images because of its over-locality. Thus, the other discontinuity measure to detect contextual discontinuities for feature preservation is additionally required. The local-inhomogeneity measure provides the degree of uniformity in small regions, and is able to detect locations of the significant discontinuities effectively. Therefore the authors propose a Bayesian denoising framework using the two complementary discontinuity measures. The two complementary discontinuity measures are elaborately combined to be employed for creating prior probabilities of the Bayesian denoising framework. The experimental results show that the proposed method not only achieves a high peak signal to noise ratio (PSNR) gain from noisy images but also reduces noise effectively while preserving edge components.