# Patch Craft: Video Denoising by Deep Modeling and Patch Matching

arXiv: https://arxiv.org/abs/2103.13767 · HTML: https://mlsanity.com/paper/2103.13767/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://mlsanity.com/paper/1904.00962/) (2019)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [MemNet: A Persistent Memory Network for Image Restoration](https://mlsanity.com/paper/1708.02209/) (2017)
- [Multi-level Wavelet-CNN for Image Restoration](https://mlsanity.com/paper/1805.07071/) (2018)
- [Residual Dense Network for Image Restoration](https://mlsanity.com/paper/1812.10477/) (2018)
- [Non-Local Recurrent Network for Image Restoration](https://mlsanity.com/paper/1806.02919/) (2018)
- [FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation](https://mlsanity.com/paper/1907.01361/) (2019)
- [Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes](https://mlsanity.com/paper/2003.14013/) (2020)
- [DVDnet: A Fast Network for Deep Video Denoising](https://mlsanity.com/paper/1906.11890/) (2019)
- [ViDeNN: Deep Blind Video Denoising](https://mlsanity.com/paper/1904.10898/) (2019)
- [Non-Local Color Image Denoising with Convolutional Neural Networks](https://mlsanity.com/paper/1611.06757/) (2016)
- [Model-blind Video Denoising Via Frame-to-frame Training](https://mlsanity.com/paper/1811.12766/) (2018)
- [On the Global-Local Dichotomy in Sparsity Modeling](https://mlsanity.com/paper/1702.03446/) (2017)

## Built on by (most cited first, 30 of 38)

- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [Recurrent Video Restoration Transformer with Guided Deformable Attention](https://mlsanity.com/paper/2206.02146/) (2022)
- [Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --](https://mlsanity.com/paper/2301.03362/) (2023)
- [A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift](https://mlsanity.com/paper/2206.10810/) (2022)
- [Dancing under the stars: video denoising in starlight](https://mlsanity.com/paper/2204.04210/) (2022)
- [Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones](https://mlsanity.com/paper/2204.05532/) (2022)
- [Skip-Attention: Improving Vision Transformers by Paying Less Attention](https://mlsanity.com/paper/2301.02240/) (2023)
- [On the Generalization of BasicVSR++ to Video Deblurring and Denoising](https://mlsanity.com/paper/2204.05308/) (2022)
- [Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review](https://mlsanity.com/paper/2308.00247/) (2023)
- [Real-time Streaming Video Denoising with Bidirectional Buffers](https://mlsanity.com/paper/2207.06937/) (2022)
- [Learning Truncated Causal History Model for Video Restoration](https://mlsanity.com/paper/2410.03936/) (2024)
- [Real-time Controllable Denoising for Image and Video](https://mlsanity.com/paper/2303.16425/) (2023)
- [Recurrent Self-Supervised Video Denoising with Denser Receptive Field](https://mlsanity.com/paper/2308.03608/) (2023)
- [Temporal As a Plugin: Unsupervised Video Denoising with Pre-Trained Image Denoisers](https://mlsanity.com/paper/2409.11256/) (2024)
- [Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising](https://mlsanity.com/paper/2208.11803/) (2022)
- [Patch-Craft Self-Supervised Training for Correlated Image Denoising](https://mlsanity.com/paper/2211.09919/) (2022)
- [Spatiotemporal Blind-Spot Network with Calibrated Flow Alignment for Self-Supervised Video Denoising](https://mlsanity.com/paper/2412.11820/) (2024)
- [Coarse-to-Fine Video Denoising with Dual-Stage Spatial-Channel Transformer](https://mlsanity.com/paper/2205.00214/) (2022)
- [Quanta Video Restoration](https://mlsanity.com/paper/2410.14994/) (2024)
- [Grounding Degradations in Natural Language for All-In-One Video Restoration](https://mlsanity.com/paper/2507.14851/) (2025)
- [Lightweight Video Denoising Using a Classic Bayesian Backbone](https://mlsanity.com/paper/2408.03904/) (2024)
- [Reversing the Damage: A QP-Aware Transformer-Diffusion Approach for 8K Video Restoration under Codec Compression](https://mlsanity.com/paper/2412.08912/) (2024)
- [Classic Video Denoising in a Machine Learning World: Robust, Fast, and Controllable](https://mlsanity.com/paper/2504.03136/) (2025)
- [Joint Flow And Feature Refinement Using Attention For Video Restoration](https://mlsanity.com/paper/2505.16434/) (2025)
- [TRNR: Task-Driven Image Rain and Noise Removal with a Few Images Based on Patch Analysis](https://mlsanity.com/paper/2112.01924/) (2021)
- [Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques](https://mlsanity.com/paper/2507.08375/) (2025)
- [Joint denoising and HDR for RAW video sequences](https://mlsanity.com/paper/2201.07066/) (2022)
- [Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Network](https://mlsanity.com/paper/2207.01356/) (2022)
- [Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration](https://mlsanity.com/paper/2303.16411/) (2023)
- [Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep Learning](https://mlsanity.com/paper/2305.18708/) (2023)

## Abstract (arXiv)

The non-local self-similarity property of natural images has been exploited extensively for solving various image processing problems. When it comes to video sequences, harnessing this force is even more beneficial due to the temporal redundancy. In the context of image and video denoising, many classically-oriented algorithms employ self-similarity, splitting the data into overlapping patches, gathering groups of similar ones and processing these together somehow. With the emergence of convolutional neural networks (CNN), the patch-based framework has been abandoned. Most CNN denoisers operate on the whole image, leveraging non-local relations only implicitly by using a large receptive field. This work proposes a novel approach for leveraging self-similarity in the context of video denoising, while still relying on a regular convolutional architecture. We introduce a concept of patch-craft frames - artificial frames that are similar to the real ones, built by tiling matched patches. Our algorithm augments video sequences with patch-craft frames and feeds them to a CNN. We demonstrate the substantial boost in denoising performance obtained with the proposed approach.