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：最近的深度图像增强方法通常可以分为两种类型：分解与增强和以照明估算为中心的方法。前者通常效率较低，后者受到有关将反射率作为所需增强结果的强烈假设的限制。为了缓解这种限制，同时保持高效性，我们提出了一种新颖的可训练模块，它通过相机响应函数和曝光补偿比率参数化为复参数方程，使得图像增强具有更大的多样性。通过将这个模块纳入以光照估算为中心的深度神经网络中，我们的方法提高了深度图像增强的灵活性，将计算负担限制在光照估算上，并允许完全无监督的学习，适应不同任务的各种需求。