Abstract: Video contains both intra-frame spatial correlation and inter-frame temporal correlation.When reconstructing high-re-solution video from low-resolution video,adjacent multi-frame information can be aligned to guide the current frame recovery.Deformable convolution guided by optical flow is commonly used for explicit frame-by-frame alignment,this method overcomes the instability of deformable convolution,but will affect the recovery of high-frequency information in the frame,reduce the accuracy of the alignment information and magnify artifacts.To address these issues,this paper proposes IAVSR(Implicit Alignment Video Super-Resolution),a video super-resolution model based on implicit alignment.IAVSR encodes optical flow to specific pixel positions using offset and original values,calculating pre-alignment information instead of interpolating.Deformable convolution is used to realign pre-aligned features and recover high-frequency information.Bidirectional propagation uses information from the first two frames to guide current frame recovery,while a residual network structure improves alignment accuracy and avoids excessive parameter introduction.Experimental results on the REDS4 public dataset show that IAVSR achieves 0.6 dB higher PSNR value than the benchmark models and improves model convergence speed by 20% during training.

Key words: Video super resolution, Deformable convolution, Re-sampling, Implicit alignment, Optical flow