Single image blind deblurring via adapting total generalized variation

Abstract

This paper introduces a fast blind deconvolution strategy for image deblurring by modifying a recent natural image model, i.e., the total generalized variation (TGV), which aims at reconstructing an image with higher-order smoothness as well as sharp edges. But, when it turns to the blind issue, as demonstrated either empirically or theoretically by a few previous blind deblurring works, natural image models including TGV are more often than not inclined to trivial solutions, e.g., the delta blur kernel and the input blurred images. Inspired by the discovery, a simple, yet effective modifying strategy is applied to the second-order TGV, leading to a novel l 0 –l 1 -norm-based image regularization adaptable to the blind deblurring problem. Then, a numerical algorithm is deduced with O(NlogN) complexity, via borrowing ideas of the operator splitting method, the augmented Lagrangian, and also the fast Fourier transform (FFT). Experimental results on both synthetic and real color blurred images demonstrate the superiority or comparable performance of the new approach to state-of-the-art ones, in terms of deblurring accuracy and efficiency.