Temporal Coherent Video Super-resolution via Pre-frame-constrained Sparse Reconstruction

Qiuxia Lai, Yongwei Nie, Zhensong Zhang, Hanqiu Sun

Abstract: In this paper, we extend the sparse representation based image super-resolution method to process videos, mainly aiming at obtaining temporally consistent consecutive high-resolution (HR) video frames. In our formulation, the previous estimated HR frame is used to guide the sparse reconstruction of current low-resolution (LR) frame, which is able to obtain more consistent representations. We show that such guidance is robust and effective by incorporating with a non-rigid dense correspondence based motion compensation schema. We also propose a dictionary updating strategy which regularly updates the dictionaries that are critical for the sparse representation procedure using the newly reconstructed HR frames. To further preserve sharp edges and remove reconstruction errors, once a HR image is recovered, we refine it with a L0-norm based optimization that constrains the final HR output with relatively sparse gradients. Experimental results on natural videos demonstrated the effectiveness of our proposed method.