Fusion of regularization terms for image restoration

Abstract

We propose an efficient regularized restoration model associating a spatial and a frequential regularizer in order to better model the intrinsic properties of the original image to be recovered and to obtain a better restoration result. An adaptive and rescaling scheme is also proposed to balance the influence of these two dif- ferent regularization constraints, preventing an overwhelming impor- tance for one of them from prevailing over the other, enabling them to be efficiently fused during the iterative deconvolution process. This hybrid regularization approach, mixing these two constraints and, more precisely, favoring a solution image that is both efficiently denoised (due to the denoising ability of a thresholding procedure in the discrete cosine transform (DCT) domain) and edge-preserved (due to the generalized Gaussian Markov random field (GGMRF) constraint); yields significant improvements in terms of image quality and higher signal-to-noise ratio improvement results compared to a single GGMRF or DCT prior model and leads to competitive resto- ration results in benchmark tests, for various levels of blur, blurred signal to noise ratio (BSNR), and noise degradations. © 2010 SPIE