TDAN: Temporally Deformable Alignment Network for Video Super-Resolution

TL;DR

This paper introduces TDAN, a novel video super-resolution method that aligns frames at the feature level using deformable convolutions, eliminating the need for optical flow and improving alignment accuracy.

Contribution

The paper proposes a deformable alignment network for VSR that adaptively aligns frames without optical flow, enhancing robustness and accuracy.

Findings

Outperforms flow-based methods in alignment accuracy.

Reduces artifacts caused by optical flow errors.

Achieves superior super-resolution quality in experiments.

Abstract

Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple neighboring frames (supporting frames). Due to varying motion of cameras or objects, the reference frame and each support frame are not aligned. Therefore, temporal alignment is a challenging yet important problem for VSR. Previous VSR methods usually utilize optical flow between the reference frame and each supporting frame to wrap the supporting frame for temporal alignment. Therefore, the performance of these image-level wrapping-based models will highly depend on the prediction accuracy of optical flow, and inaccurate optical flow will lead to artifacts in the wrapped supporting frames, which also will be propagated into the reconstructed HR video frame. To overcome the limitation, in this paper, we propose a temporal deformable alignment network (TDAN) to adaptively align the reference frame and each supporting frame at the feature level without computing optical flow. The TDAN uses features from both the reference frame and each supporting frame to dynamically predict offsets of sampling convolution kernels. By using the corresponding kernels, TDAN transforms supporting frames to align with the reference frame. To predict the HR video frame, a reconstruction network taking aligned frames and the reference frame is utilized. Experimental results demonstrate the effectiveness of the proposed TDAN-based VSR model.