# A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement

arXiv: https://arxiv.org/abs/1711.00591 · HTML: https://mlsanity.com/paper/1711.00591/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2017.

## Built on by (most cited first, 30 of 38)

- [Low-Light Image Enhancement with Normalizing Flow](https://mlsanity.com/paper/2109.05923/) (2021)
- [Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method](https://mlsanity.com/paper/2212.11548/) (2022)
- [Learning Enriched Features for Fast Image Restoration and Enhancement](https://mlsanity.com/paper/2205.01649/) (2022)
- [R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network](https://mlsanity.com/paper/2106.14501/) (2021)
- [Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression](https://mlsanity.com/paper/2207.10564/) (2022)
- [Towards Low Light Enhancement with RAW Images](https://mlsanity.com/paper/2112.14022/) (2021)
- [LLDiffusion: Learning Degradation Representations in Diffusion Models for Low-Light Image Enhancement](https://mlsanity.com/paper/2307.14659/) (2023)
- [Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance](https://mlsanity.com/paper/2309.08382/) (2023)
- [Debiased Subjective Assessment of Real-World Image Enhancement](https://mlsanity.com/paper/2106.10080/) (2021)
- [Denoising Diffusion Post-Processing for Low-Light Image Enhancement](https://mlsanity.com/paper/2303.09627/) (2023)
- [Unrolled Decomposed Unpaired Learning for Controllable Low-Light Video Enhancement](https://mlsanity.com/paper/2408.12316/) (2024)
- [Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning](https://mlsanity.com/paper/2104.01888/) (2021)
- [DA-DRN: Degradation-Aware Deep Retinex Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2110.01809/) (2021)
- [Extremely Low-light Image Enhancement with Scene Text Restoration](https://mlsanity.com/paper/2204.00630/) (2022)
- [Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement](https://mlsanity.com/paper/2207.00965/) (2022)
- [Text in the Dark: Extremely Low-Light Text Image Enhancement](https://mlsanity.com/paper/2404.14135/) (2024)
- [Linear Array Network for Low-light Image Enhancement](https://mlsanity.com/paper/2201.08996/) (2022)
- [DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2209.06823/) (2022)
- [From Darkness to Detail: Frequency-Aware SSMs for Low-Light Vision](https://mlsanity.com/paper/2408.09650/) (2024)
- [RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image Enhancement](https://mlsanity.com/paper/2409.04363/) (2024)
- [Low Light Image Enhancement via Global and Local Context Modeling](https://mlsanity.com/paper/2101.00850/) (2021)
- [Quality Assessment of Low Light Restored Images: A Subjective Study and an Unsupervised Model](https://mlsanity.com/paper/2202.02277/) (2022)
- [Semi-supervised atmospheric component learning in low-light image problem](https://mlsanity.com/paper/2204.07546/) (2022)
- [DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier Convolution for Low-light Image Enhancement](https://mlsanity.com/paper/2209.07937/) (2022)
- [Low-Light Enhancement in the Frequency Domain](https://mlsanity.com/paper/2306.16782/) (2023)
- [Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details](https://mlsanity.com/paper/2101.08039/) (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)
- [Low-light Image and Video Enhancement via Selective Manipulation of Chromaticity](https://mlsanity.com/paper/2203.04889/) (2022)
- [Deep Decomposition and Bilinear Pooling Network for Blind Night-Time Image Quality Evaluation](https://mlsanity.com/paper/2205.05880/) (2022)
- [KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image](https://mlsanity.com/paper/2207.09210/) (2022)

## Abstract (arXiv)

Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. Although many image enhancement techniques have been proposed to solve this problem, existing methods inevitably introduce contrast under- and over-enhancement. Inspired by human visual system, we design a multi-exposure fusion framework for low-light image enhancement. Based on the framework, we propose a dual-exposure fusion algorithm to provide an accurate contrast and lightness enhancement. Specifically, we first design the weight matrix for image fusion using illumination estimation techniques. Then we introduce our camera response model to synthesize multi-exposure images. Next, we find the best exposure ratio so that the synthetic image is well-exposed in the regions where the original image is under-exposed. Finally, the enhanced result is obtained by fusing the input image and the synthetic image according to the weight matrix. Experiments show that our method can obtain results with less contrast and lightness distortion compared to that of several state-of-the-art methods.