Adaptive incremental video super-resolution with temporal consistency
Video super-resolution can be generally divided into two categories: incremental video super-resolution and simultaneous video super-resolution. Incremental video super-resolution algorithms are usually faster, but their results cannot be guaranteed to be visually consistent to the human vision system. An adaptive incremental video super-resolution framework with the temporal consistency constraint is proposed in this paper. The temporal consistency among the video frames is enforced by imposing the similarity between the adjacent reconstructed HR frames. The variances of the potential functions, which affect the weights of the different terms in the utility function, are adaptively determined so that the algorithm is robust to various motion and image content situations. Some considerations, such as the incremental motion estimation, are also incorporated to improve the efficiency of the algorithm, which makes the proposed algorithm near-realtime. The experimental results show that the proposed algorithm can generate HR video with high quality while saving the computational time as well.