Fast Image Deblurring Based on Dual-Exposure Prior
Xu Zhang, Hai‐Miao Hu, Jialin Chen · 2019
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 Dual-Exposure 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 L2-regularized DEP in the maximum a posterior framework. Each sub-problem of the optimization is convex, it can be solved in close-form with fast Fourier transforms acceleration. The experiments based on real and synthesized blurry images show that the proposed image deblurring algorithm can outperform the state-of-the-art methods in terms of subjective and objective quality. Moreover, the proposed algorithm can be implemented with significantly low computational complexity.