In this paper, we make the first benchmark effort to elaborate on the superiority of using RAW images in the low light enhancement and develop a novel alternative route to utilize RAW images in a more flexible and practical way. Inspired by a full consideration on the typical image processing pipeline, we are inspired to develop a new evaluation framework, Factorized Enhancement Model (FEM), which decomposes the properties of RAW images into measurable factors and provides a tool for exploring how properties of RAW images affect the enhancement performance empirically. The empirical benchmark results show that the Linearity of data and Exposure Time recorded in meta-data play the most critical role, which brings distinct performance gains in various measures over the approaches taking the sRGB images as input. With the insights obtained from the benchmark results in mind, a RAW-guiding Exposure Enhancement Network (REENet) is developed, which makes trade-offs between the advantages and inaccessibility of RAW images in real applications in a way of using RAW images only in the training phase. REENet projects sRGB images into linear RAW domains to apply constraints with corresponding RAW images to reduce the difficulty of modeling training. After that, in the testing phase, our REENet does not rely on RAW images. Experimental results demonstrate not only the superiority of REENet to state-of-the-art sRGB-based methods and but also the effectiveness of the RAW guidance and all components.
翻译:在本文中,我们首次作出基准努力,阐述在低光度增强中使用RAW图像的优越性,并开发一种以更灵活、更实际的方式使用RAW图像的新替代途径。在对典型图像处理管道进行充分考虑的启发下,我们受启发开发一个新的评价框架,即将RAW图像的属性分解成可计量因素的加分增强模型(FEM),为探索RAW图像的特性如何以经验方式影响提高绩效提供了一个工具。经验基准结果显示,在元数据中记录的数据和曝光时间的线性化和曝光时间的线性能发挥最关键的作用,在以SRGB图像作为投入的方法的各种措施上带来不同的绩效收益收益。根据从基准结果获得的深刻见解,我们开发了一个RAW-Guard E 曝光增强网络(RENet) 网络(RENet) 的优点和可获取性能之间的取舍,只有培训阶段才使用RAW图像。RENet项目将SRB图像应用到线性拉布域,从而将限制与相应的RAW图像的成份部分应用来减少模型升级的难度。之后,在SRAV-RAW的所有阶段测试阶段中不只进行。