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Despite the quality improvement brought by the recent methods, videosuper-resolution (SR) is still very challenging, especially for videos that arelow-light and noisy. The current best solution is to subsequently employ bestmodels of video SR, denoising, and illumination enhancement, but doing so oftenlowers the image quality, due to the inconsistency between the models. Thispaper presents a new parametric representation called the Deep Parametric 3DFilters (DP3DF), which incorporates local spatiotemporal information to enablesimultaneous denoising, illumination enhancement, and SR efficiently in asingle encoder-and-decoder network. Also, a dynamic residual frame is jointlylearned with the DP3DF via a shared backbone to further boost the SR quality.We performed extensive experiments, including a large-scale user study, to showour method's effectiveness. Our method consistently surpasses the beststate-of-the-art methods on all the challenging real datasets with top PSNR anduser ratings, yet having a very fast run time.

Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution