# Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement

arXiv: https://arxiv.org/abs/2302.11464 · HTML: https://mlsanity.com/paper/2302.11464/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 77 references that are in the map.
- First posted on arXiv 22 Feb 2023.

## Builds on (most cited first, 24 of 24)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [PieAPP: Perceptual Image-Error Assessment through Pairwise Preference](https://mlsanity.com/paper/1806.02067/) (2018)
- [Learning Enriched Features for Fast Image Restoration and Enhancement](https://mlsanity.com/paper/2205.01649/) (2022)
- [MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2004.05508/) (2020)
- [Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment](https://mlsanity.com/paper/2008.03889/) (2020)
- [Learning Generalized Spatial-Temporal Deep Feature Representation for No-Reference Video Quality Assessment](https://mlsanity.com/paper/2012.13936/) (2020)
- [Joint Enhancement and Denoising Method via Sequential Decomposition](https://mlsanity.com/paper/1804.08468/) (2018)
- [Self-supervised Image Enhancement Network: Training with Low Light Images Only](https://mlsanity.com/paper/2002.11300/) (2020)
- [Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network](https://mlsanity.com/paper/2012.15052/) (2020)
- [The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement](https://mlsanity.com/paper/2202.09738/) (2022)

## Built on by (most cited first, 7 of 7)

- [All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model](https://mlsanity.com/paper/2411.07445/) (2024)
- [RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image Enhancement](https://mlsanity.com/paper/2409.04363/) (2024)
- [EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience](https://mlsanity.com/paper/2604.08063/) (2026)
- [Sliced Maximal Information Coefficient: A Training-Free Approach for Image Quality Assessment Enhancement](https://mlsanity.com/paper/2408.09920/) (2024)
- [VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models: Methods and Results](https://mlsanity.com/paper/2509.09190/) (2025)
- [Preference-Guided Debiasing for No-Reference Enhancement Image Quality Assessment](https://mlsanity.com/paper/2603.20086/) (2026)
- [BlindPSNR: A No-Reference Fidelity Predictor for Low-Light Image Enhancement](https://mlsanity.com/paper/2607.27628/) (2026)

## Abstract (arXiv)

There is a growing consensus in the research community that the optimization of low-light image enhancement approaches should be guided by the visual quality perceived by end users. Despite the substantial efforts invested in the design of low-light enhancement algorithms, there has been comparatively limited focus on assessing subjective and objective quality systematically. To mitigate this gap and provide a clear path towards optimizing low-light image enhancement for better visual quality, we propose a gap-closing framework. In particular, our gap-closing framework starts with the creation of a large-scale dataset for Subjective QUality Assessment of REconstructed LOw-Light Images (SQUARE-LOL). This database serves as the foundation for studying the quality of enhanced images and conducting a comprehensive subjective user study. Subsequently, we propose an objective quality assessment measure that plays a critical role in bridging the gap between visual quality and enhancement. Finally, we demonstrate that our proposed objective quality measure can be incorporated into the process of optimizing the learning of the enhancement model toward perceptual optimality. We validate the effectiveness of our proposed framework through both the accuracy of quality prediction and the perceptual quality of image enhancement. Our database and codes are publicly available at https://github.com/Baoliang93/IACA_For_Lowlight_IQA.