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Repository Details

Collection of popular and reproducible video denoising works.

reproducible-video-denoising-state-of-the-art

Collection of popular and reproducible video denoising works.

Criteria: works must have codes available, and the reproducible results which demonstrate promising or state-of-the-art performances for video denoising.

This format of the collection is similar to reproducible-image-denoising

Please feel free to contribute to this repo.

Video Denoising Algorithms

Online Methods

  • ReLD [Web] [Code] [PDF]
    • Video denoising via online sparse and lowrank matrix decomposition (SSP 2016), Guo and Vaswani.
  • VIDOSAT [Web] [Code] [PDF]
    • VIDOSAT - High-dimensional Sparsifying Transform Learning for Online Video Restoration (TIP 2019), Wen et al.

Non-Local Methods

  • VBM3D [Web] [Matlab] [Python] [PDF]
    • Video denoising by sparse 3D transform-domain collaborative filtering (EUSIPCO 2007), Dabov et al.
  • VBM4D [Web] [Code] [PDF]
    • Video Denoising, Deblocking and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms (TIP 2012), Maggioni et al.
  • RNLF [Web] [Code] [PDF]
    • Adaptive regularization of the NL-means: Application to image and video denoising (TIP 2014), Sutour et al.
  • SALT [Web] [Code] [PDF]
    • Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising (ICCV 2017), Wen et al.

Bayesian Approach

  • VNLB [Web] [Code] [PDF]
    • Video Denoising via Empirical Bayesian Estimation of Space-Time Patches (JMIV 2017), Arias and Morel

Deep Learning

  • VNLNet [Web] [Code] [PDF]
    • Non-Local Video Denoising by CNN (Arxiv 2018), Davy et al.
  • FastDVDnet [Web] [Code] [PDF]
    • FastDVDnet: Towards Real-Time Video Denoising Without Explicit Motion Estimation (Arxiv 2019), Tassano et al.
  • LDK [Web] [Code] [PDF]
    • Learning Deformable Kernels for Image and Video Denoising (Arxiv 2019), Xu et al.
  • EDVR [Web] [Code] [PDF]
    • Video Restoration with Enhanced Deformable Convolutional Networks (CVPRW 2019), Wang et al.
  • TOF [Web] [Code] [PDF]
    • Video Enhancement with Task-Oriented Flow (IJCV 2019), Xue et al.

Model-blind Learning

  • BlindDenoising [Web] [Code] [PDF]
    • Model-Blind Video Denoising via Frame-To-Frame Training (CVPR 2019), Tassano et al.
  • ViDeNN [Web] [Code] [PDF]
    • ViDeNN: Deep Blind Video Denoising (CVPRW 2019), Claus and Gemert