Image Deblurring Via Combined Total Variation and Framelet

Abstract

This paper presents a new approach to image deblurring, on the basis of total variation (TV) and wavelet frame. The Rudin---Osher---Fatemi model, which is based on TV minimization, has been proven effective for image restoration. The explicit exploitation of sparse approximations of natural images has led to the success of wavelet frame approach in solving image restoration problems. However, TV introduces staircase effects. Thus, we propose a new objective functional that combines the tight wavelet frame and TV to reconstruct images from blurry and noisy observations while mitigating staircase effects. The minimization of the new objective functional presents a computational challenge. We propose a fast minimization algorithm by employing the augmented Lagrangian technique. The experiments on a set of image deblurring benchmark problems show that the proposed method outperforms the previous state-of-the-art methods for image restoration.