论文标题
深白平衡编辑
Deep White-Balance Editing
论文作者
论文摘要
我们介绍了一种深度学习方法，以现实地编辑SRGB图像的白平衡。相机捕获由其集成信号处理器（ISP）渲染到标准RGB（SRGB）颜色空间编码的传感器图像。 ISP渲染始于白平衡过程，该过程用于去除场景照明的颜色铸件。然后，ISP应用一系列非线性颜色操作来增强最终SRGB图像的视觉质量。 [3]的最新工作表明，由于ISP的非线性渲染，用不正确的白平衡渲染的SRGB图像无法轻易纠正。 [3]中的工作提出了基于成千上万的图像对的k-neartient邻居（KNN）解决方案。我们建议通过以端到端训练的深度神经网络（DNN）体系结构来解决这个问题，以学习正确的白平衡。我们的DNN将输入图像映射到两个与室内和室外照明相对应的其他白色体重设置。我们的解决方案不仅在纠正错误的白色平衡设置方面比KNN方法更准确，而且还为用户提供了编辑SRGB图像中的白平衡的自由，以将其编辑为其他照明设置。
We introduce a deep learning approach to realistically edit an sRGB image's white balance. Cameras capture sensor images that are rendered by their integrated signal processor (ISP) to a standard RGB (sRGB) color space encoding. The ISP rendering begins with a white-balance procedure that is used to remove the color cast of the scene's illumination. The ISP then applies a series of nonlinear color manipulations to enhance the visual quality of the final sRGB image. Recent work by [3] showed that sRGB images that were rendered with the incorrect white balance cannot be easily corrected due to the ISP's nonlinear rendering. The work in [3] proposed a k-nearest neighbor (KNN) solution based on tens of thousands of image pairs. We propose to solve this problem with a deep neural network (DNN) architecture trained in an end-to-end manner to learn the correct white balance. Our DNN maps an input image to two additional white-balance settings corresponding to indoor and outdoor illuminations. Our solution not only is more accurate than the KNN approach in terms of correcting a wrong white-balance setting but also provides the user the freedom to edit the white balance in the sRGB image to other illumination settings.
