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.

翻译：研究界日益达成共识：暗光图像增强方法的优化应以终端用户感知的视觉质量为导向。尽管在暗光增强算法设计方面已投入大量努力，但在系统评估主观与客观质量方面的关注相对有限。为弥合这一差距并为优化暗光图像增强的视觉质量提供清晰路径，本文提出一种差距填补框架。具体而言，我们的框架始于构建大规模"重建暗光图像主观质量评估"数据集。该数据库为研究增强图像质量及开展全面主观用户研究奠定了基础。随后，我们提出一种客观质量评估指标，该指标在连接视觉质量与增强效果之间发挥着关键作用。最后，我们证明所提出的客观质量指标可融入增强模型的学习优化过程，以实现感知最优性。通过质量预测的准确性和图像增强的感知质量两方面，我们验证了所提框架的有效性。我们的数据库与代码已公开于 https://github.com/Baoliang93/IACA_For_Lowlight_IQA。