Brighten-and-Colorize：一种用于定制化低光图像增强的解耦网络
Brighten-and-Colorize: A Decoupled Network for Customized Low-Light Image Enhancement
AI总结：
本文提出BCNet，将低光图像增强分解为提亮与上色两个解耦子任务，利用低光色度作为可操作的颜色引导，在实现SOTA增强性能的同时支持用户定制饱和度和颜色风格。
摘要：
低光图像增强（LLIE）旨在提升在低光条件下拍摄图像的感知质量。通常，低光图像可分为亮度分量和色度分量。该领域的最新进展主要集中在亮度分量的细化上，而忽视了色度的作用。这容易导致色差，并在一定程度上限制了色度在定制化LLIE中的多样化应用。在本工作中，提出了一种“先提亮再上色”网络（称为BCNet），将图像上色引入LLIE以解决上述问题。BCNet能够在实现精确颜色的LLIE的同时，根据用户偏好实现不同饱和度和颜色风格的定制化增强。具体而言，BCNet将LLIE视为一个多任务学习问题：提亮和上色。提亮子任务与其他传统LLIE方法一致，以获得光照良好的亮度。上色子任务通过将低光图像的色度作为颜色引导（类似于用户引导图像上色）来完成。模型训练完成后，用户可以简单地对颜色引导（即输入的低光色度）进行操作，以获得定制化结果。该定制化过程是可选的，并且由于其解耦特性，不会损害亮度的结构和细节信息。在常用LLIE数据集上的大量实验表明，所提方法同时实现了最先进（SOTA）性能和用户友好的定制化。
英文摘要：
Low-Light Image Enhancement (LLIE) aims to improve the perceptual quality of an image captured in low-light conditions. Generally, a low-light image can be divided into lightness and chrominance components. Recent advances in this area mainly focus on the refinement of the lightness, while ignoring the role of chrominance. It easily leads to chromatic aberration and, to some extent, limits the diverse applications of chrominance in customized LLIE. In this work, a ``brighten-and-colorize'' network (called BCNet), which introduces image colorization to LLIE, is proposed to address the above issues. BCNet can accomplish LLIE with accurate color and simultaneously enables customized enhancement with varying saturations and color styles based on user preferences. Specifically, BCNet regards LLIE as a multi-task learning problem: brightening and colorization. The brightening sub-task aligns with other conventional LLIE methods to get a well-lit lightness. The colorization sub-task is accomplished by regarding the chrominance of the low-light image as color guidance like the user-guide image colorization. Upon completion of model training, the color guidance (i.e., input low-light chrominance) can be simply manipulated by users to acquire customized results. This customized process is optional and, due to its decoupled nature, does not compromise the structural and detailed information of lightness. Extensive experiments on the commonly used LLIE datasets show that the proposed method achieves both State-Of-The-Art (SOTA) performance and user-friendly customization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
