论文标题
深入学习，渲染天然摄像头景点
Rendering Natural Camera Bokeh Effect with Deep Learning
论文作者
论文摘要
Bokeh是一种重要的艺术效果，用于通过模糊所有聚焦区域来突出照片上关注的主要对象。尽管数码单反相机和系统摄像机镜头可以自然产生这种效果，但由于其光学元件的光圈直径很小，移动相机无法产生较浅的视野照片。与当前的解决方案通过将高斯模糊应用于图像背景的当前解决方案不同，在本文中，我们建议直接从DSLR摄像机拍摄的照片中学习一种现实的浅焦点技术。为此，我们提供了一个大型散景数据集，该数据集由使用50mm f / 1.8镜头的佳能7D DSLR捕获的5K浅 /宽深度图像对。我们使用这些图像来训练深度学习模型，以基于单个窄孔图像来重现天然的散景效果。实验结果表明，即使在具有多个对象的复杂输入数据的情况下，该提出的方法也能够呈现出合理的非均匀散布。本文使用的数据集，预培训的模型和代码可在项目网站上找到。
Bokeh is an important artistic effect used to highlight the main object of interest on the photo by blurring all out-of-focus areas. While DSLR and system camera lenses can render this effect naturally, mobile cameras are unable to produce shallow depth-of-field photos due to a very small aperture diameter of their optics. Unlike the current solutions simulating bokeh by applying Gaussian blur to image background, in this paper we propose to learn a realistic shallow focus technique directly from the photos produced by DSLR cameras. For this, we present a large-scale bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR with 50mm f/1.8 lenses. We use these images to train a deep learning model to reproduce a natural bokeh effect based on a single narrow-aperture image. The experimental results show that the proposed approach is able to render a plausible non-uniform bokeh even in case of complex input data with multiple objects. The dataset, pre-trained models and codes used in this paper are available on the project website.
