Siggraph 2017，深度双边学习进行实时图像增强，Deep Bilateral Learning for Real-Time Image Enhancement
摘要：
Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.
摘要：性能是移动端图像处理的一个关键挑战。给定一个参考图像，甚至是人为调整的图像对，我们试图重现增强功能并实现实时评估。为此，我们介绍一种新的神经网络架构，灵感来自于双边网格处理和局部仿射颜色变换。使用输入/输出图像对，我们训练卷积神经网络来预测双边空间中局部仿射模型的系数。我们的架构，学习制定局部、全局和内容依赖的决策来近似所需的图像转换。在运行时，神经网络消耗输入图像的低分辨率版本，在双边空间中产生一组仿射变换，使用新的切片节点以边缘保留方式对这些变换进行上采样，然后将这些上采样变换应用于全分辨率图像。我们的算法以毫秒为单位在智能手机上处理高分辨率图像，提供1080p分辨率的实时取景器，并且匹配大类图像运算符上的最先进的近似技术的质量。与以前的工作不同，我们的模型是从数据离线训练的，因此在运行时不需要访问原始操作。这允许我们的模型学习复杂的，场景相关的变换，没有参考实现可用，例如人类润饰器的照相编辑
项目主页：
https://groups.csail.mit.edu/graphics/hdrnet/
原文链接：
https://m.weibo.cn/5501429448/4137686992139540
