# DALE : Dark Region-Aware Low-light Image Enhancement

arXiv: https://arxiv.org/abs/2008.12493 · HTML: https://mlsanity.com/paper/2008.12493/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction](https://mlsanity.com/paper/2309.00872/) (2023)
- [DEAL: Data-Efficient Adversarial Learning for High-Quality Infrared Imaging](https://mlsanity.com/paper/2503.00905/) (2025)
- [Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction](https://mlsanity.com/paper/2309.01183/) (2023)
- [LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset](https://mlsanity.com/paper/2503.19804/) (2025)
- [Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networks](https://mlsanity.com/paper/2204.01795/) (2022)

## Abstract (arXiv)

In this paper, we present a novel low-light image enhancement method called dark region-aware low-light image enhancement (DALE), where dark regions are accurately recognized by the proposed visual attention module and their brightness are intensively enhanced. Our method can estimate the visual attention in an efficient manner using super-pixels without any complicated process. Thus, the method can preserve the color, tone, and brightness of original images and prevents normally illuminated areas of the images from being saturated and distorted. Experimental results show that our method accurately identifies dark regions via the proposed visual attention, and qualitatively and quantitatively outperforms state-of-the-art methods.