Bundled Kernels for Nonuniform Blind Video Deblurring

Abstract

We present a novel blind video deblurring approach by estimating a bundle of kernels and applying the residual deconvolution. Our approach adopts multiple kernels to represent spatially varying motion blur, and thus can cope with nonuniform video deblurring. For each blurred frame, we build a warping-based, space-variant motion blur model based on a bundle of homographies in between its adjacent frames. Then, the nearest sharp frame is employed to form an unblurred-blurred pair for solving the motion model and obtain a bundle of kernels at the blurred frame. Finally, we apply the deconvolution on the residual between the warped unblurred frame and blurred frame with the kernels. The blur kernel estimation and residual deconvolution are iteratively performed toward the deblurred frame, as well as significantly reducing artifacts such as ringings. Experiments show that our approach can efficiently remove the nonuniform video blurring, and achieves better deblurring results than some state-of-the-art methods.