Imaging in the natural scene under ill lighting conditions (e.g., low light, back-lit, over-exposed front-lit, and any combinations of them) suffers from both over- and under-exposure at the same time, whereas processing of such images often results in over- and under-enhancement. A single small image sensor can hardly provide satisfactory quality for ill lighting conditions with ordinary optical lenses in capturing devices. Challenges arise in the maintenance of a visual smoothness between those regions, while color and contrast should be well preserved. The problem has been approached by various methods, including multiple sensors and handcrafted parameters, but extant model capacity is limited to only some specific scenes (i.e., lighting conditions). Motivated by these challenges, in this paper, we propose a deep image enhancement method for color images captured under ill lighting conditions. In this method, input images are first decomposed into reflection and illumination maps with the proposed layer distribution loss net, where the illumination blindness and structure degradation problem can be subsequently solved via these two components, respectively. The hidden degradation in reflection and illumination is tuned with a knowledge-based adaptive enhancement constraint designed for ill illuminated images. The model can maintain a balance of smoothness and contribute to solving the problem of noise besides over- and under-enhancement. The local consistency in illumination is achieved via a repairing operation performed in the proposed Repair-Net. The total variation operator is optimized to acquire local consistency, and the image gradient is guided with the proposed enhancement constraint. Finally, a product of updated reflection and illumination maps reconstructs an enhanced image. Experiments are organized under both very low exposure and ill illumination conditions, where a new dataset is also proposed. Results on both experiments show that our method has superior performance in preserving structural and textural details compared to other states of the art, which suggests that our method is more practical in future visual applications.

中文翻译：

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用于不良光成像的深度图像增强

在不良照明条件下（例如，弱光、背光、前光过度曝光以及它们的任何组合）在自然场景中成像同时遭受过度曝光和曝光不足的影响，而处理此类图像通常会导致过度增强和增强不足。单个小型图像传感器很难在拍摄设备中使用普通光学镜头在光线不足的情况下提供令人满意的质量。在保持这些区域之间的视觉平滑度方面存在挑战，同时应很好地保留颜色和对比度。该问题已通过多种方法解决，包括多个传感器和手工参数，但现有模型容量仅限于某些特定场景（即光照条件）。受这些挑战的推动，在本文中，我们提出了一种在光线不足的情况下捕获的彩色图像的深度图像增强方法。在该方法中，输入图像首先被分解为反射图和光照图layer distribution loss net，其中照明盲和结构退化问题可以通过这两个组件分别解决。反射和照明中隐藏的退化通过为照明不良图像设计的基于知识的自适应增强约束进行调整。该模型可以保持平滑度的平衡，有助于解决除增强过度和增强不足之外的噪声问题。照明的局部一致性是通过在提议的修复网络中执行的修复操作来实现的. 优化总变异算子以获得局部一致性，并以建议的增强约束引导图像梯度。最后，更新的反射和照明图的乘积重建了增强的图像。实验是在非常低的曝光和光线不足的条件下组织的，其中还提出了一个新的数据集。两个实验的结果表明，与其他现有技术相比，我们的方法在保留结构和纹理细节方面具有卓越的性能，这表明我们的方法在未来的视觉应用中更加实用。