mlsanityData to 7 Oct 2026

Rank-One Prior: Toward Real-Time Scene Recovery

arXiv 2103.17126 ↗First posted 31 Mar 2021cs.CVcited by 7

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Abstract (arXiv)

Scene recovery is a fundamental imaging task for several practical applications, e.g., video surveillance and autonomous vehicles, etc. To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze. The heart of our work is that we propose an intensity projection strategy to estimate the transmission. This strategy is motivated by a straightforward rank-one transmission prior. The complexity of transmission estimation is O(N) where N is the size of the single image. Then we can recover the scene in real-time. Comprehensive experiments on different types of weather/imaging conditions illustrate that our method outperforms competitively several state-of-the-art imaging methods in terms of efficiency and robustness.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 10 of its 55 references that are in the map. First posted on arXiv 31 Mar 2021.

Builds on

10 of 55 references.

References · 10

  1. 2017Benchmarking Single Image Dehazing and Beyondcited by 445
  2. 2016DehazeNet: An End-to-End System for Single Image Haze Removalcited by 313
  3. 2019An Underwater Image Enhancement Benchmark Dataset and Beyondcited by 250
  4. 2019Dense Haze: A benchmark for image dehazing with dense-haze and haze-free imagescited by 107
  5. 2018O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor imagescited by 95
  6. 2018I-HAZE: a dehazing benchmark with real hazy and haze-free indoor imagescited by 64
  7. 2018Unsupervised Single Image Dehazing Using Dark Channel Prior Losscited by 30
  8. 2020MLFcGAN: Multi-level Feature Fusion based Conditional GAN for Underwater Image Color Correctioncited by 8
  9. 2019Variational Regularized Transmission Refinement for Image Dehazingcited by 4
  10. 2013Dense Scattering Layer Removalcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 0; 2025: 1; 2026: 1.

7 papers.