Video superresolution reconstruction based on subpixel registration and iterative back projection

To improve the spatial resolution of video, a superresolution reconstruction method based on a sliding window is proposed utilizing the movement information between frames in the low-resolution video. We propose a registration algorithm based on a four-parameter transformation model through Taylor series expansion, using an iterative solving method as well as the Gaussian pyramid image model to estimate the movement parameters from coarseness to fine. Superresolution frames are reconstructed using an iterative back projection (IBP) algorithm. We also present the suitable length of the sliding window and the reasonable iteration number of the IBP algorithm in the video superresolution reconstruction. Our algorithm is compared to other algorithms on simulated images and actual color videos. Both show that our registration algorithm achieves higher subpixel accuracy than other algorithms, even in the case of large movements, and that the reconstructed video has better visual effects and stronger resolution ability. It can be extensively applied to the superresolution reconstruction of video sequences in which the frames are different from each other mainly by translation and rotation.