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

arXiv: https://arxiv.org/abs/2207.01797 · HTML: https://mlsanity.com/paper/2207.01797/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 50 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019)
- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://mlsanity.com/paper/2104.13371/) (2021)
- [BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond](https://mlsanity.com/paper/2012.02181/) (2020)
- [TDAN: Temporally Deformable Alignment Network for Video Super-Resolution](https://mlsanity.com/paper/1812.02898/) (2018)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [Frame-Recurrent Video Super-Resolution](https://mlsanity.com/paper/1801.04590/) (2018)
- [Burst Denoising with Kernel Prediction Networks](https://mlsanity.com/paper/1712.02327/) (2017)
- [Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation](https://mlsanity.com/paper/1611.05250/) (2016)
- [Detail-revealing Deep Video Super-resolution](https://mlsanity.com/paper/1704.02738/) (2017)
- [Recurrent Back-Projection Network for Video Super-Resolution](https://mlsanity.com/paper/1903.10128/) (2019)
- [FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation](https://mlsanity.com/paper/1907.01361/) (2019)
- [DeepLPF: Deep Local Parametric Filters for Image Enhancement](https://mlsanity.com/paper/2003.13985/) (2020)
- [Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution](https://mlsanity.com/paper/2002.11616/) (2020)
- [Video Super-resolution with Temporal Group Attention](https://mlsanity.com/paper/2007.10595/) (2020)
- [DVDnet: A Fast Network for Deep Video Denoising](https://mlsanity.com/paper/1906.11890/) (2019)
- [Basis Prediction Networks for Effective Burst Denoising with Large Kernels](https://mlsanity.com/paper/1912.04421/) (2019)
- [ViDeNN: Deep Blind Video Denoising](https://mlsanity.com/paper/1904.10898/) (2019)
- [Model-blind Video Denoising Via Frame-to-frame Training](https://mlsanity.com/paper/1811.12766/) (2018)
- [Learning for Video Super-Resolution through HR Optical Flow Estimation](https://mlsanity.com/paper/1809.08573/) (2018)
- [Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments](https://mlsanity.com/paper/2012.05116/) (2020)

## Built on by (most cited first, 10 of 10)

- [Geometric-Aware Low-Light Image and Video Enhancement via Depth Guidance](https://mlsanity.com/paper/2312.15855/) (2023)
- [Boosting Image Restoration via Priors from Pre-trained Models](https://mlsanity.com/paper/2403.06793/) (2024)
- [Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition](https://mlsanity.com/paper/2405.15660/) (2024)
- [RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image Enhancement](https://mlsanity.com/paper/2409.04363/) (2024)
- [Enhancing Diffusion-based Restoration Models via Difficulty-Adaptive Reinforcement Learning with IQA Reward](https://mlsanity.com/paper/2511.01645/) (2025)
- [Optimizing 4D Lookup Table for Low-light Video Enhancement via Wavelet Priori](https://mlsanity.com/paper/2409.08585/) (2024)
- [Boosting Fidelity for Pre-Trained-Diffusion-Based Low-Light Image Enhancement via Condition Refinement](https://mlsanity.com/paper/2510.17105/) (2025)
- [Row-Column Separated Attention Based Low-Light Image/Video Enhancement](https://mlsanity.com/paper/2602.07428/) (2026)
- [Low-Light Video Enhancement with An Effective Spatial-Temporal Decomposition Paradigm](https://mlsanity.com/paper/2602.08699/) (2026)
- [OLiVES: An Outdoor Low-Light Video Benchmark for Enhancement and Segmentation](https://mlsanity.com/paper/2610.20501/) (2026)

## Abstract (arXiv)

Despite the quality improvement brought by the recent methods, video super-resolution (SR) is still very challenging, especially for videos that are low-light and noisy. The current best solution is to subsequently employ best models of video SR, denoising, and illumination enhancement, but doing so often lowers the image quality, due to the inconsistency between the models. This paper presents a new parametric representation called the Deep Parametric 3D Filters (DP3DF), which incorporates local spatiotemporal information to enable simultaneous denoising, illumination enhancement, and SR efficiently in a single encoder-and-decoder network. Also, a dynamic residual frame is jointly learned with the DP3DF via a shared backbone to further boost the SR quality. We performed extensive experiments, including a large-scale user study, to show our method's effectiveness. Our method consistently surpasses the best state-of-the-art methods on all the challenging real datasets with top PSNR and user ratings, yet having a very fast run time.