Efficient Joint Video Denoising and Super-Resolution

Denoising and super-resolution are two important tasks for video enhancement. Despite recent progress for each task, there are very few works that target both tasks simultaneously. In this paper, we propose an efficient noise-robust video super-resolution method that is trained end-to-end for an input video containing observable noises. We investigate current approaches to address this joint denoising and super-resolution task and compare them to our proposed method. Experimental results show that our method achieves competitive reconstruction performance with existing solutions on various datasets while maintaining a low computation cost and a small model size which prove the effectiveness of our joint model design and training. Our code is available at "https://github.com/Eventhyn/EVDSRNet.".