FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring
†Co-corresponding authors
1Korea Advanced Institute of Science and Technology, South Korea
2Chung-Ang University, South Korea
This repository is the official PyTorch implementation of "FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring". FMA-Net achieves state-of-the-art performance in joint video super-resolution and deblurring (VSRDB).
Please visit our project page for more visual results.
Geunhyuk Youk, Jihyong Oh† and Munchurl Kim† "FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring", arXiv, 2024. (†: Co-corresponding authors)
@article{youk2024fmanet,
author = {Geunhyuk Youk and Jihyong Oh and Munchurl Kim},
title = {FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring},
journal = {arXiv preprint arXiv:2401.03707},
year = {2024},
}