论文标题
无监督的夜间图像增强：当层分解符合光效应时
Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression
论文作者
论文摘要
夜间图像不仅遭受低光，而且遭受光线分布不均匀的影响。大多数现有的夜间可见性增强方法主要集中在增强弱光区域。这不可避免地会导致明亮区域的过度增强和饱和度，例如那些受光效应（眩光，泛光灯等）影响的区域。为了解决这个问题，我们需要抑制明亮区域的光效应，同时促进黑暗区域的强度。考虑到这个想法，我们引入了一种无监督的方法，该方法集成了层分解网络和光效应抑制网络。给定单夜图像作为输入，我们的分解网络学会了分解阴影，反射率和光效应层，并在无监督的特定层特定的先前损失的指导下。我们的光效应抑制网络进一步抑制了光效应，同时增强了黑暗区域的照明。该光效应抑制网络利用了估计的光效应层，作为专注于光效应区域的指导。为了恢复背景细节并减少幻觉/人工制品，我们提出了结构和高频一致性损失。我们对真实图像的定量和定性评估表明，我们的方法在抑制夜光效应和提高黑暗区域的强度方面优于最先进的方法。
Night images suffer not only from low light, but also from uneven distributions of light. Most existing night visibility enhancement methods focus mainly on enhancing low-light regions. This inevitably leads to over enhancement and saturation in bright regions, such as those regions affected by light effects (glare, floodlight, etc). To address this problem, we need to suppress the light effects in bright regions while, at the same time, boosting the intensity of dark regions. With this idea in mind, we introduce an unsupervised method that integrates a layer decomposition network and a light-effects suppression network. Given a single night image as input, our decomposition network learns to decompose shading, reflectance and light-effects layers, guided by unsupervised layer-specific prior losses. Our light-effects suppression network further suppresses the light effects and, at the same time, enhances the illumination in dark regions. This light-effects suppression network exploits the estimated light-effects layer as the guidance to focus on the light-effects regions. To recover the background details and reduce hallucination/artefacts, we propose structure and high-frequency consistency losses. Our quantitative and qualitative evaluations on real images show that our method outperforms state-of-the-art methods in suppressing night light effects and boosting the intensity of dark regions.
