Dynamic Scene Video Deblurring Using Robust Incremental Weighted Fourier Aggregation

Motion blur is an inevitable problem when shooting a video in motion, and it will cause serious degradation of images. In this letter, we propose a new robust incremental weighted Fourier aggregation algorithm for dynamic scene video deblurring. This is motivated by the fact that existing multiframe aggregation methods usually require a tedious iterative process to obtain sharp information in distant frames. We propose to perform alignment and aggregation once for each frame using the current frame and the previous frame result, which includes the sharp information of all the previous frames. Moreover, a criterion for evaluating the alignment of blurred images is proposed. Based on this, a robust aggregation approach is also proposed to help reduce artifacts caused by image patch alignment failure. Specifically, we decompose the current frame into a series of image patches, find consistent image patches in the deblurring results of the previous frame by a coarse-to-fine alignment algorithm, and aggregate them in the Fourier domain. Experiments show that our method can obtain improved deblurring results and reduce the computational complexity compared to the traditional aggregation method.