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Abstract：
Low-light images are prevalent in intelligent monitoring and many other applications, with low brightness hindering further processing. Although low-light image enhancement can reduce the influence of such problems, current methods often involve a complex network structure or many iterations, which are not conducive to their efficiency. This paper proposes a Zero-Reference Camera Response Network using a camera response model to achieve efficient enhancement for arbitrary low-light images. A double-layer parameter-generating network with a streamlined structure is established to extract the exposure ratio K from the radiation map, which is obtained by inverting the input through a camera response function. Then, K is used as the parameter of a brightness transformation function for one transformation on the low-light image to realize enhancement. In addition, a contrast-preserving brightness loss and an edge-preserving smoothness loss are designed without the requirement for references from the dataset. Both can further retain some key information in the inputs to improve precision. The enhancement is simplified and can reach more than twice the speed of similar methods. Extensive experiments on several LLIE datasets and the DARK FACE face detection dataset fully demonstrate our method\'s advantages, both subjectively and objectively.
摘要：
微光图像在智能监控和许多其他应用中普遍存在，低亮度阻碍进一步加工。尽管弱光图像增强可以减少此类问题的影响，当前的方法通常涉及复杂的网络结构或许多迭代，这不利于他们的效率。本文提出了一种使用相机响应模型的零参考相机响应网络，以实现对任意弱光图像的有效增强。建立具有流线型结构的双层参数生成网络，从辐射图中提取曝光率K，这是通过相机响应函数反转输入获得的。然后，K被用作用于对弱光图像进行一次变换以实现增强的亮度变换函数的参数。此外,设计了对比度保持亮度损失和边缘保持平滑度损失，而无需从数据集中引用。两者都可以进一步在输入中保留一些关键信息以提高精度。该增强被简化并且可以达到类似方法的两倍以上的速度。在多个LLIE数据集和DARKFACE人脸检测数据集上的大量实验充分证明了我们方法的优势,主观和客观。
