# Dual Illumination Estimation for Robust Exposure Correction

arXiv: https://arxiv.org/abs/1910.13688 · HTML: https://mlsanity.com/paper/1910.13688/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2019.

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

- [Learning to Adapt to Light](https://mlsanity.com/paper/2202.08098/) (2022)
- [Progressive Joint Low-light Enhancement and Noise Removal for Raw Images](https://mlsanity.com/paper/2106.14844/) (2021)
- [LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion](https://mlsanity.com/paper/2307.02452/) (2023)
- [Color Shift Estimation-and-Correction for Image Enhancement](https://mlsanity.com/paper/2405.17725/) (2024)
- [Noise Self-Regression: A New Learning Paradigm to Enhance Low-Light Images Without Task-Related Data](https://mlsanity.com/paper/2211.04700/) (2022)
- [LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-Diffusion](https://mlsanity.com/paper/2405.10550/) (2024)
- [Debiased Subjective Assessment of Real-World Image Enhancement](https://mlsanity.com/paper/2106.10080/) (2021)
- [Exposure Correction Model to Enhance Image Quality](https://mlsanity.com/paper/2204.10648/) (2022)
- [A Lightweight Low-Light Image Enhancement Network via Channel Prior and Gamma Correction](https://mlsanity.com/paper/2402.18147/) (2024)
- [Specularity Factorization for Low-Light Enhancement](https://mlsanity.com/paper/2404.01998/) (2024)
- [Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language Guidance](https://mlsanity.com/paper/2405.03333/) (2024)
- [Practical exposure correction via compensation](https://mlsanity.com/paper/2212.14245/) (2022)
- [Simultaneous Enhancement and Noise Suppression under Complex Illumination Conditions](https://mlsanity.com/paper/2512.08378/) (2025)
- [SALVE: Self-supervised Adaptive Low-light Video Enhancement](https://mlsanity.com/paper/2212.11484/) (2022)
- [A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models](https://mlsanity.com/paper/2207.02396/) (2022)
- [Illuminating Darkness: Learning to Enhance Low-light Images In-the-Wild](https://mlsanity.com/paper/2503.06898/) (2025)
- [Robust Low-light Scene Restoration via Illumination Transition](https://mlsanity.com/paper/2507.03976/) (2025)
- [From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning](https://mlsanity.com/paper/2507.08380/) (2025)
- [ReF-LLE: Personalized Low-Light Enhancement via Reference-Guided Deep Reinforcement Learning](https://mlsanity.com/paper/2506.22216/) (2025)
- [Endo-4DGX: Robust Endoscopic Scene Reconstruction and Illumination Correction with Gaussian Splatting](https://mlsanity.com/paper/2506.23308/) (2025)
- [Deep Perceptual Enhancement for Medical Image Analysis](https://mlsanity.com/paper/2503.08027/) (2025)
- [BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with Noise Removal and Color Restoration](https://mlsanity.com/paper/2106.15953/) (2021)
- [Low-light Image and Video Enhancement via Selective Manipulation of Chromaticity](https://mlsanity.com/paper/2203.04889/) (2022)
- [NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge Transfer](https://mlsanity.com/paper/2402.17159/) (2024)
- [ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image Enhancement](https://mlsanity.com/paper/2407.19708/) (2024)
- [Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction](https://mlsanity.com/paper/2504.10080/) (2025)
- [LoopExpose: An Unsupervised Framework for Arbitrary-Length Exposure Correction](https://mlsanity.com/paper/2511.06066/) (2025)
- [ZeroIDIR: Zero-Reference Illumination Degradation Image Restoration with Perturbed Consistency Diffusion Models](https://mlsanity.com/paper/2605.11435/) (2026)
- [SLAM in Low-Light Environments: Project Report](https://mlsanity.com/paper/2607.17699/) (2026)
- [High-Quality Exposure Correction with Diffusion-Based Image Generation Priors](https://mlsanity.com/paper/2608.08720/) (2026)

## Abstract (arXiv)

Exposure correction is one of the fundamental tasks in image processing and computational photography. While various methods have been proposed, they either fail to produce visually pleasing results, or only work well for limited types of image (e.g., underexposed images). In this paper, we present a novel automatic exposure correction method, which is able to robustly produce high-quality results for images of various exposure conditions (e.g., underexposed, overexposed, and partially under- and over-exposed). At the core of our approach is the proposed dual illumination estimation, where we separately cast the under- and over-exposure correction as trivial illumination estimation of the input image and the inverted input image. By performing dual illumination estimation, we obtain two intermediate exposure correction results for the input image, with one fixes the underexposed regions and the other one restores the overexposed regions. A multi-exposure image fusion technique is then employed to adaptively blend the visually best exposed parts in the two intermediate exposure correction images and the input image into a globally well-exposed image. Experiments on a number of challenging images demonstrate the effectiveness of the proposed approach and its superiority over the state-of-the-art methods and popular automatic exposure correction tools.