Blind super-resolution restoration with frame-by-frame nonparametric blur estimation

In this paper, we investigate super-resolution image restoration from multiple images, which are possibly degraded with large nonparametric motion blur. The blur kernel for each input image is separately estimated. This is unlike many existing super-resolution algorithms, which assume identical blur kernel for all input images. We also do not make any restrictions on the motion field among images; that is, we estimate dense motion field without simplifications such as parametric motion. We present a two-step algorithm: In the first step, each input image is deblurred using its estimated blur kernel. In the second step, multi-frame super-resolution restoration is applied to the deblurred images. Because the estimated blur kernels may not be accurate, we propose a weighted cost function for the super-resolution restoration step, where a weight associated with an input image reflects the reliabilities of the corresponding kernel estimate and deblurred image. We provide experimental results with both simulated and real data, and show the effectiveness and robustness of the proposed method compared to some alternative approaches and state-of-the-art methods.