Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model
Abstract
Low-light image enhancement methods based on classic Retinex model attempt to manipulate the estimated illumination and to project it back to the corresponding reflectance. However, the model does not consider the noise, which inevitably exists in images captured in low-light conditions. In this paper, we propose the robust Retinex model, which additionally considers a noise map compared with the conventional Retinex model, to improve the performance of enhancing low-light images accompanied by intensive noise. Based on the robust Retinex model, we present an optimization function that includes novel regularization terms for the illumination and reflectance. Specifically, we use norm to constrain the piece-wise smoothness of the illumination, adopt a fidelity term for gradients of the reflectance to reveal the structure details in low-light images, and make the first attempt to estimate a noise map out of the robust Retinex model. To effectively solve the optimization problem, we provide an augmented Lagrange multiplier based alternating direction minimization algorithm without logarithmic transformation. Experimental results demonstrate the effectiveness of the proposed method in low-light image enhancement. In addition, the proposed method can be generalized to handle a series of similar problems, such as the image enhancement for underwater or remote sensing and in hazy or dusty conditions.
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Low-light images usually contain noises that will be amplified after enhancement, motivating robust Retinex models that account for noise and estimate reflectance, illumination, and noise simultaneously.
“Since lowlight images usually contain noises that will be amplified after enhancement, some robust Retinex models have been proposed to account for noise and estimate reflectance, illumination, and noise simultaneously [147], [148].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [147]
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Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model
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- Authors
- Li, M., Liu, J., Yang, W., Sun, X., & Guo, Z.
- Affiliations
- Peking University (CN)
- Microsoft Research Asia (China) (CN)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2018-06
- Volume
- 27
- Issue
- 6
- Pages
- 2828-2841
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2791710889
- Cited by
- 1,245
- References
- 42
- Citations by year
- 2026: 108 · 2025: 221 · 2024: 220 · 2023: 206 · 2022: 214 · 2021: 133 · 2020: 90 · 2019: 41 · 2018: 12
- FWCI
- 34.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61772043)
- Microsoft Research Asia (ID FY17-RES-THEME-013)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- low-light image enhancement, Retinex model, regularization, structure preservation, augmented Lagrange multiplier, underwater image enhancement, remote sensing image enhancement
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