mlsanityData to 7 Oct 2026

SALVE: Self-supervised Adaptive Low-light Video Enhancement

arXiv 2212.11484 ↗First posted 22 Dec 2022cs.CV eess.IVcited by 4

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Abstract (arXiv)

A self-supervised adaptive low-light video enhancement method, called SALVE, is proposed in this work. SALVE first enhances a few key frames of an input low-light video using a retinex-based low-light image enhancement technique. For each keyframe, it learns a mapping from low-light image patches to enhanced ones via ridge regression. These mappings are then used to enhance the remaining frames in the low-light video. The combination of traditional retinex-based image enhancement and learning-based ridge regression leads to a robust, adaptive and computationally inexpensive solution to enhance low-light videos. Our extensive experiments along with a user study show that 87% of participants prefer SALVE over prior work.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 40 references that are in the map. First posted on arXiv 22 Dec 2022.

Builds on

20 of 40 references.

References · 20

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  12. 2019The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentationcited by 95
8 more
  1. 2019Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Datasetcited by 65
  2. 2019Dual Illumination Estimation for Robust Exposure Correctioncited by 30
  3. 2022Green Learning: Introduction, Examples and Outlookcited by 30
  4. 2019Single-frame Regularization for Temporally Stable CNNscited by 21
  5. 2021Successive Subspace Learning: An Overviewcited by 11
  6. 2022PAGER: Progressive Attribute-Guided Extendable Robust Image Generationcited by 5
  7. 2020Noise-Aware Texture-Preserving Low-Light Enhancementcited by 4
  8. 2016The communication-hiding pipelined BiCGStab method for the parallel solution of large unsymmetric linear systemscited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2023FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware Lookup Tablecited by 15
  2. 2024Unrolled Decomposed Unpaired Learning for Controllable Low-Light Video Enhancementcited by 7
  3. 2024RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image Enhancementcited by 2
  4. 2025Removing Multiple Hybrid Adverse Weather in Video via a Unified Modelcited by 0