# Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network

arXiv: https://arxiv.org/abs/2012.15020 · HTML: https://mlsanity.com/paper/2012.15020/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2020.

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

- [MAXIM: Multi-Axis MLP for Image Processing](https://mlsanity.com/paper/2201.02973/) (2022)
- [Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method](https://mlsanity.com/paper/2212.11548/) (2022)
- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [CLE Diffusion: Controllable Light Enhancement Diffusion Model](https://mlsanity.com/paper/2308.06725/) (2023)
- [CSformer: Bridging Convolution and Transformer for Compressive Sensing](https://mlsanity.com/paper/2112.15299/) (2021)
- [ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field](https://mlsanity.com/paper/2312.09095/) (2023)
- [Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics](https://mlsanity.com/paper/2405.18790/) (2024)
- [TSCnet: A Text-driven Semantic-level Controllable Framework for Customized Low-Light Image Enhancement](https://mlsanity.com/paper/2503.08168/) (2025)
- [Learning Diverse Tone Styles for Image Retouching](https://mlsanity.com/paper/2207.05430/) (2022)
- [Neural Color Operators for Sequential Image Retouching](https://mlsanity.com/paper/2207.08080/) (2022)
- [PromptLNet: Region-Adaptive Aesthetic Enhancement via Prompt Guidance in Low-Light Enhancement Net](https://mlsanity.com/paper/2503.08276/) (2025)
- [NamedCurves: Learned Image Enhancement via Color Naming](https://mlsanity.com/paper/2407.09892/) (2024)
- [Exposure Correction Model to Enhance Image Quality](https://mlsanity.com/paper/2204.10648/) (2022)
- [Semantic-guided Multi-Mask Image Harmonization](https://mlsanity.com/paper/2207.11722/) (2022)
- [Misalignment-Robust Frequency Distribution Loss for Image Transformation](https://mlsanity.com/paper/2402.18192/) (2024)
- [Specularity Factorization for Low-Light Enhancement](https://mlsanity.com/paper/2404.01998/) (2024)
- [Generalized Visual Quality Assessment of GAN-Generated Face Images](https://mlsanity.com/paper/2201.11975/) (2022)
- [Learning-Based Video Coding with Joint Deep Compression and Enhancement](https://mlsanity.com/paper/2111.14474/) (2021)
- [Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement](https://mlsanity.com/paper/2207.00965/) (2022)
- [Generalized Lightness Adaptation with Channel Selective Normalization](https://mlsanity.com/paper/2308.13783/) (2023)
- [Learning Adaptive Lighting via Channel-Aware Guidance](https://mlsanity.com/paper/2412.01493/) (2024)
- [Brighten-and-Colorize: A Decoupled Network for Customized Low-Light Image Enhancement](https://mlsanity.com/paper/2308.03029/) (2023)
- [The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement](https://mlsanity.com/paper/2202.09738/) (2022)
- [Region-Aware Portrait Retouching with Sparse Interactive Guidance](https://mlsanity.com/paper/2304.04017/) (2023)
- [A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation](https://mlsanity.com/paper/2402.18402/) (2024)
- [Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection](https://mlsanity.com/paper/2607.08674/) (2026)
- [ExReg: Wide-range Photo Exposure Correction via a Multi-dimensional Regressor with Attention](https://mlsanity.com/paper/2212.14801/) (2022)
- [Unsupervised network for low-light enhancement](https://mlsanity.com/paper/2306.02883/) (2023)
- [Just Noticeable Difference Modeling for Face Recognition System](https://mlsanity.com/paper/2209.05856/) (2022)
- [Unsupervised haze removal from underwater images](https://mlsanity.com/paper/2306.02912/) (2023)

## Abstract (arXiv)

Improving the aesthetic quality of images is challenging and eager for the public. To address this problem, most existing algorithms are based on supervised learning methods to learn an automatic photo enhancer for paired data, which consists of low-quality photos and corresponding expert-retouched versions. However, the style and characteristics of photos retouched by experts may not meet the needs or preferences of general users. In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner, rather than learning on a large number of paired images. The proposed model is based on single deep GAN which embeds the modulation and attention mechanisms to capture richer global and local features. Based on the proposed model, we introduce two losses to deal with the unsupervised image enhancement: (1) fidelity loss, which is defined as a L2 regularization in the feature domain of a pre-trained VGG network to ensure the content between the enhanced image and the input image is the same, and (2) quality loss that is formulated as a relativistic hinge adversarial loss to endow the input image the desired characteristics. Both quantitative and qualitative results show that the proposed model effectively improves the aesthetic quality of images. Our code is available at: https://github.com/eezkni/UEGAN.