Bokeh effect is a natural shallow depth-of-field phenomenon that blurs the out-of-focus part in photography. In recent years, a series of works have proposed automatic and realistic bokeh rendering methods for artistic and aesthetic purposes. They usually employ cutting-edge data-driven deep generative networks with complex training strategies and network architectures. However, these works neglect that the bokeh effect, as a real phenomenon, can inevitably affect the subsequent visual intelligent tasks like recognition, and their data-driven nature prevents them from studying the influence of bokeh-related physical parameters (i.e., depth-of-the-field) on the intelligent tasks. To fill this gap, we study a totally new problem, i.e., natural & adversarial bokeh rendering, which consists of two objectives: rendering realistic and natural bokeh and fooling the visual perception models (i.e., bokeh-based adversarial attack). To this end, beyond the pure data-driven solution, we propose a hybrid alternative by taking the respective advantages of data-driven and physical-aware methods. Specifically, we propose the circle-of-confusion predictive network (CoCNet) by taking the all-in-focus image and depth image as inputs to estimate circle-of-confusion parameters for each pixel, which are employed to render the final image through a well-known physical model of bokeh. With the hybrid solution, our method could achieve more realistic rendering results with the naive training strategy and a much lighter network.

翻译：散景效应是一种自然的浅景深现象，可使摄影中的失焦部分产生模糊效果。近年来，一系列研究提出了用于艺术与美学目的的自动逼真散景渲染方法。这些方法通常采用先进的数据驱动深度生成网络，并配备复杂的训练策略与网络架构。然而，此类研究忽视了散景效应作为真实现象不可避免会影响后续视觉智能任务（如识别）这一事实；同时其数据驱动特性也阻碍了散景相关物理参数（即景深）对智能任务影响的研究。为填补这一空白，我们研究了一个全新问题——自然与对抗性散景渲染，该问题包含两个目标：生成真实自然的散景效果，以及欺骗视觉感知模型（即基于散景的对抗攻击）。为此，我们超越纯粹数据驱动方案，提出融合数据驱动方法与物理感知方法各自优势的混合替代方案。具体而言，我们提出弥散圆预测网络（CoCNet），以全聚焦图像和深度图像为输入，逐像素估计弥散圆参数，并通过经典的散景物理模型渲染最终图像。借助这种混合方案，我们的方法能以简单的训练策略和更轻量的网络实现更逼真的渲染效果。