Fast Image Deblurring Based on DualExposure Prior Image deblurring is an important task for practical applications. However, it remains a challenge due to the serious camera shake. In this paper, we propose a prior named DualExposure Prior (DEP) according to the observation that image captured with a relatively short exposure time may achieve sharp edges. Based on the DEP, the blur kernels of the blurry images can be accurately estimated. An effective optimization between blur kernel estimation and intermediate image restoration is established by using the L 2 regularized DEP in the maximum a posterior framework. Each subproblem of the optimization is convex, it can be solved in closeform with fast Fourier transforms acceleration. The experiments based on real and synthesized blurry images show that the proposed image deblurring algorithm can outperform the stateoftheart methods in terms of subjective and objective quality. Moreover, the proposed algorithm can be implemented with significantly low computational complexity.

A Coarsetofine Classification for Motion Blur Kernel Size Estimation with Cascaded Neural Networks Blur kernel size estimation significantly affects the quality of image deblurring. However, existing methods have a large limitation in estimation accuracy as well as in the adaptability to large kernel sizes. In this paper, we propose a novel blur kernel size estimation method using CNNs. We first convert the 2D kernel size estimation to a 1D classification problem. A cascaded network model using homogeneous networks is then designed to cut the entire estimation space into 38 small intervals to have a more accurate kernel size estimation in a coarsetofine manner. Experiments show that the proposed method has reached a satisfying performance of kernel size estimation with a largely improved adaptability to various blur kernels.