Spatial-scale-based blur kernel estimation for blind motion deblurring
Shu Gang Tang, Xianzhong Xie, Xiao Luan, Ming Xia, Peisong Liu · 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) · 2017
Maximum a posteriori (MAP)-based single-image blind motion deblurring methods are extensively studied in the past years, and have achieved great progress. However, because of imperfect salient edges selection, most state-of-the-art methods still cannot estimate the blur kernel (BK) accurately, especially in large motion blur cases. In this paper, we propose a novel spatial-scale-based approach to estimate an accurate BK from a single motion blurred image by combining the spatial scale and L0norm. Furthermore, we propose an efficient optimization strategy which can solve the proposed model efficiently. Extensive experiments compared with state-of-the-art blind motion deblurring methods demonstrate the effectiveness of our method.