# STAR: A Structure and Texture Aware Retinex Model

arXiv: https://arxiv.org/abs/1906.06690 · HTML: https://mlsanity.com/paper/1906.06690/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2019.

## Built on by (most cited first, 30 of 41)

- [Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach](https://mlsanity.com/paper/2404.00834/) (2024)
- [DarkIR: Robust Low-Light Image Restoration](https://mlsanity.com/paper/2412.13443/) (2024)
- [PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition](https://mlsanity.com/paper/2203.16670/) (2022)
- [Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning](https://mlsanity.com/paper/2211.14751/) (2022)
- [DMFourLLIE: Dual-Stage and Multi-Branch Fourier Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2412.00683/) (2024)
- [EvLight++: Low-Light Video Enhancement with an Event Camera: A Large-Scale Real-World Dataset, Novel Method, and More](https://mlsanity.com/paper/2408.16254/) (2024)
- [Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction](https://mlsanity.com/paper/2309.00872/) (2023)
- [Practical exposure correction via compensation](https://mlsanity.com/paper/2212.14245/) (2022)
- [DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement](https://mlsanity.com/paper/2404.03327/) (2024)
- [Simultaneous Enhancement and Noise Suppression under Complex Illumination Conditions](https://mlsanity.com/paper/2512.08378/) (2025)
- [Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction](https://mlsanity.com/paper/2309.01183/) (2023)
- [IGDNet: Zero-Shot Robust Underexposed Image Enhancement via Illumination-Guided and Denoising](https://mlsanity.com/paper/2507.02445/) (2025)
- [Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark](https://mlsanity.com/paper/2207.03367/) (2022)
- [FLOL: Fast Baselines for Real-World Low-Light Enhancement](https://mlsanity.com/paper/2501.09718/) (2025)
- [Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image Enhancement](https://mlsanity.com/paper/2502.19867/) (2025)
- [EvRWKV: A Continuous Interactive RWKV Framework for Effective Event-Guided Low-Light Image Enhancement](https://mlsanity.com/paper/2507.03184/) (2025)
- [Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset](https://mlsanity.com/paper/2605.22186/) (2026)
- [FCNet: A Convolutional Neural Network for Arbitrary-Length Exposure Estimation](https://mlsanity.com/paper/2203.03624/) (2022)
- [SIGNet: Intrinsic Image Decomposition by a Semantic and Invariant Gradient Driven Network for Indoor Scenes](https://mlsanity.com/paper/2208.14369/) (2022)
- [Low-light Stereo Image Enhancement and De-noising in the Low-frequency Information Enhanced Image Space](https://mlsanity.com/paper/2401.07753/) (2024)
- [LUMINA-Net: Low-light Upgrade through Multi-stage Illumination and Noise Adaptation Network for Image Enhancement](https://mlsanity.com/paper/2502.15186/) (2025)
- [IllumFlow: Illumination-Adaptive Low-Light Enhancement via Conditional Rectified Flow and Retinex Decomposition](https://mlsanity.com/paper/2511.02411/) (2025)
- [An Optical physics inspired CNN approach for intrinsic image decomposition](https://mlsanity.com/paper/2105.10076/) (2021)
- [NLHD: A Pixel-Level Non-Local Retinex Model for Low-Light Image Enhancement](https://mlsanity.com/paper/2106.06971/) (2021)
- [BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with Noise Removal and Color Restoration](https://mlsanity.com/paper/2106.15953/) (2021)
- [Invariant Descriptors for Intrinsic Reflectance Optimization](https://mlsanity.com/paper/2204.04076/) (2022)
- [Toward Fast, Flexible, and Robust Low-Light Image Enhancement](https://mlsanity.com/paper/2204.10137/) (2022)
- [Deep Quantigraphic Image Enhancement via Comparametric Equations](https://mlsanity.com/paper/2304.02285/) (2023)
- [Dual Degradation-Inspired Deep Unfolding Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2308.02776/) (2023)
- [Revealing Shadows: Low-Light Image Enhancement Using Self-Calibrated Illumination](https://mlsanity.com/paper/2312.15199/) (2023)

## Abstract (arXiv)

Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives are attributed to the changes in reflectance, while smaller derivatives are emerged in the smooth illumination. In this paper, we utilize exponentiated local derivatives (with an exponent γ) of an observed image to generate its structure map and texture map. The structure map is produced by been amplified with γ > 1, while the texture map is generated by been shrank with γ < 1. To this end, we design exponential filters for the local derivatives, and present their capability on extracting accurate structure and texture maps, influenced by the choices of exponents γ. The extracted structure and texture maps are employed to regularize the illumination and reflectance components in Retinex decomposition. A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image. We solve the STAR model by an alternating optimization algorithm. Each sub-problem is transformed into a vectorized least squares regression, with closed-form solutions. Comprehensive experiments on commonly tested datasets demonstrate that, the proposed STAR model produce better quantitative and qualitative performance than previous competing methods, on illumination and reflectance decomposition, low-light image enhancement, and color correction. The code is publicly available at https://github.com/csjunxu/STAR.