Most recent methods of deep image enhancement can be generally classified into two types: decompose-and-enhance and illumination estimation-centric. The former is usually less efficient, and the latter is constrained by a strong assumption regarding image reflectance as the desired enhancement result. To alleviate this constraint while retaining high efficiency, we propose a novel trainable module that diversifies the conversion from the low-light image and illumination map to the enhanced image. It formulates image enhancement as a comparametric equation parameterized by a camera response function and an exposure compensation ratio. By incorporating this module in an illumination estimation-centric DNN, our method improves the flexibility of deep image enhancement, limits the computational burden to illumination estimation, and allows for fully unsupervised learning adaptable to the diverse demands of different tasks.

翻译：近期基于深度学习的图像增强方法主要可分为两类：分解增强型和光照估计主导型。前者通常效率较低，后者则受限于将图像反射率作为理想增强结果的强假设条件。为在保持高效率的同时缓解该约束，我们提出了一种新型可训练模块，该模块可实现从低光图像及光照图到增强图像的多样化转换。该方法将图像增强建模为由相机响应函数与曝光补偿比参数化的共参数方程。通过将该模块集成至光照估计主导型深度神经网络中，我们提升了深度图像增强的灵活性，将计算负担限制于光照估计环节，并支持可适应不同任务需求的无监督全自主学习。