Multi-Frame Alignment for Video Super-Resolution Using Attention

This paper presents a novel attention-based video super-resolution (VSR) method that avoids costly optical flow estimation while effectively exploiting temporal correlations between frames. We propose an aligner module that utilizes cross-attention to blend relevant patches from adjacent frames, gathering information from multiple frames simultaneously. This method improves upon traditional flow-based approaches by working at a block level and enabling the blending of several pixels, yielding better alignment for larger motions. The proposed VSR technique can upscale videos up to 4x while simultaneously removing compression artifacts, enhancing both resolution and quality. Experimental results demonstrate the effectiveness of this approach compared to classic flow-based methods, particularly in handling compressed videos where compression artifacts can severely impact optical flow estimation.