Soft-Segmentation Guided Object Motion Deblurring

Jinshan Pan, Zhe Hu, Zhixun Su, Hsin-Ying Lee, Ming-Hsuan Yang; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 459-468

Object motion blur is a challenging problem as the foreground and the background in the scenes undergo different types of image degradation due to movements in various directions and speed. Most object motion deblurring methods address this problem by segmenting blurred images into regions where different kernels are estimated and applied for restoration. Segmentation on blurred images is difficult due to ambiguous pixels between regions, but it plays an important role for object motion deblurring. To address these problems, we propose a novel model for object motion deblurring. The proposed model is developed based on a maximum a posterior formulation in which soft-segmentation is incorporated for object layer estimation. We propose an efficient algorithm to jointly estimate object segmentation and camera motion where each layer can be deblurred well under the guidance of the soft-segmentation. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art object motion deblurring methods on challenging scenarios.