FFSTIE: Video Restoration With Full-Frequency Spatio-Temporal Information Enhancement

Video distortion seriously affects user experience and downstream tasks. Existing video restoration methods still suffer from high-frequency detail loss, limited spatio-temporal dependency modeling, and high computational complexity. In this letter, we propose a novel video restoration method based on full-frequency spatio-temporal information enhancement (FFSTIE). The proposed FFSTIE includes an implicit alignment module for accurate recovery of high-frequency details and a full-frequency feature reconstruction module for adaptive enhancement of frequency components. Comprehensive experiments with quantitative and qualitative comparisons demonstrate the effectiveness of our FFSTIE method. On the video deblurring dataset DVD, FFSTIE achieves 0.75% improvement in PSNR and 1.08% improvement in SSIM with 35% fewer parameters and 59% lower GMAC compared to VDTR (TCSVT'2023), achieving a balance between performance and efficiency. On the video denoising dataset DAVIS, FFSTIE achieves the best performance with an average of 35.36 PSNR and 0.9347 SSIM, surpassing existing unsupervised methods.