Image Statistics and Local Spatial Conditions for Nonstationary Blurred Image Reconstruction

Deblurring is important in many visual systems. This paper presents a novel approach for nonstationary blurred image reconstruction with ringing reduction in a variational Bayesian learning and regularization framework. Our approach makes effective use of the image statistical prior and image local spatial conditions through the whole learning scheme. A nature image statistics based marginal prior distribution is used not only for blur kernel estimation but also for image reconstruction. For an ill-posed blur estimation problem, variational Bayesian ensemble learning can achieve a tractable posterior using an image statistic prior which is translation and scale-invariant. During the deblurring, nonstationary blurry images have stronger ringing effects. We thus propose an iterative reweighted regularization function based on the use of an image statistical prior and image local spatial conditions for perceptual image deblurring.