Multi-frame deblurring model based on low-rank tensor

Low-rank matrix approximation models have been successfully applied to numerous vision tasks. For image deblurring, the use of low-rank prior can retain the important edge information. However, for multi-frame image deblurring, the low-rank matrix models do not consider the temporal and spatial relationship among multi-frame images. In this paper, we propose a low-rank prior deblurring model based on a three-dimensional tensor, we pile up the continuous images into a tensor according to the temporal dimension, and use the tensor low-rank prior constraint, which not only maintains the dominant texture structure information to estimate the blur kernel but also considers the spatiotemporal relationship among multiple frames. We solve the energy model by alternating direction minimization method. The experimental results on different datasets show that our method can achieve better results.