论文标题
相机渲染图像的交互式白色平衡
Interactive White Balancing for Camera-Rendered Images
论文作者
论文摘要
白平衡(WB)是用于将捕获图像渲染到最终输出的第一个图像。使用WB来删除由场景的照明引起的颜色。交互式照片编辑软件允许用户在照片中手动选择不同的区域,作为WB校正照明的示例(例如,单击具有纯种的对象)。这种交互式编辑只有在以原始图像格式保存的图像中才有可能。这是因为原始图像没有应用照片渲染操作,并且照片编辑软件能够应用WB和其他照片绘制程序来渲染最终图像。相机渲染的图像中的交互式编辑WB更具挑战性。这是因为相机硬件已经将WB应用于图像和随后的非线性照片处理程序。这些非线性渲染操作使得很难更改捕获后的WB。本文的目的是允许对摄像机渲染图像进行交互式WB操纵。提出的方法是我们最近的工作\ cite {afifi2019color}的扩展,该方法基于非线性颜色映射功能提出了用于WB校正后的捕捉后方法。在这里,我们引入了一个新框架,该框架将非线性颜色映射功能直接链接到用户选择的颜色,以启用{\ it Interactive} WB操作。这个新框架在内存和运行时间方面也更有效(内存减少99 \%,加速3 $ \ times $)。最后,我们描述了我们的框架如何利用一种简单的照明估计方法(即灰色世界)进行自动WB校正,该校正与WB校正在\ cite {afifi2019color}中相当。源代码可在https://github.com/mahmoudnafifi/interactive_wb_correction上公开获得。
White balance (WB) is one of the first photo-finishing steps used to render a captured image to its final output. WB is applied to remove the color cast caused by the scene's illumination. Interactive photo-editing software allows users to manually select different regions in a photo as examples of the illumination for WB correction (e.g., clicking on achromatic objects). Such interactive editing is possible only with images saved in a RAW image format. This is because RAW images have no photo-rendering operations applied and photo-editing software is able to apply WB and other photo-finishing procedures to render the final image. Interactively editing WB in camera-rendered images is significantly more challenging. This is because the camera hardware has already applied WB to the image and subsequent nonlinear photo-processing routines. These nonlinear rendering operations make it difficult to change the WB post-capture. The goal of this paper is to allow interactive WB manipulation of camera-rendered images. The proposed method is an extension of our recent work \cite{afifi2019color} that proposed a post-capture method for WB correction based on nonlinear color-mapping functions. Here, we introduce a new framework that links the nonlinear color-mapping functions directly to user-selected colors to enable {\it interactive} WB manipulation. This new framework is also more efficient in terms of memory and run-time (99\% reduction in memory and 3$\times$ speed-up). Lastly, we describe how our framework can leverage a simple illumination estimation method (i.e., gray-world) to perform auto-WB correction that is on a par with the WB correction results in \cite{afifi2019color}. The source code is publicly available at https://github.com/mahmoudnafifi/Interactive_WB_correction.