Spatial-scale-based blur kernel estimation for blind motion deblurring

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 L0 norm. 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.