Single image super-resolution reconstruction in presence of mixed Poisson-Gaussian noise

Abstract

Single image super-resolution (SR) reconstruction aims to estimate a noise-free and blur-free high resolution image from a single blurred and noisy lower resolution observation. Most existing SR reconstruction methods assume that noise in the image is white Gaussian. Noise resulting from photon counting devices, as commonly used in image acquisition, is, however, better modelled with a mixed Poisson-Gaussian distribution. In this study we propose a single image SR reconstruction method based on energy minimization for images degraded by mixed Poisson-Gaussian noise. We evaluate performance of the proposed method on synthetic images, for different levels of blur and noise, and compare it with recent methods for non-Gaussian noise. Analysis shows that the appropriate treatment of signal-dependent noise, provided by our proposed method, leads to significant improvement in reconstruction performance.