This paper reviews the second AIM realistic bokeh effect rendering challenge
and provides the description of the proposed solutions and results. The
participating teams were solving a real-world bokeh simulation problem, where
the goal was to learn a realistic shallow focus technique using a large-scale
EBB! bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs
captured using the Canon 7D DSLR camera. The participants had to render bokeh
effect based on only one single frame without any additional data from other
cameras or sensors. The target metric used in this challenge combined the
runtime and the perceptual quality of the solutions measured in the user study.
To ensure the efficiency of the submitted models, we measured their runtime on
standard desktop CPUs as well as were running the models on smartphone GPUs.
The proposed solutions significantly improved the baseline results, defining
the state-of-the-art for practical bokeh effect rendering problem.

本文回顾了第二届 AIM 逼真的浅景深效果渲染挑战，并提供所提出的解决方案和结果的描述。参与团队通过学习大规模数据集，成功实现了只基于一个框架的实时计算和感知质量较高的浅景深效果渲染，将该领域的实际应用推向了新的高度。

AIM 2020 渲染现实晕影挑战赛

AIM 2020 Challenge on Rendering Realistic Bokeh

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.

本文提出了从 DSLR 相机制作模糊背景效果的深度学习模型，并使用大规模图像数据集进行训练。通过实验证明，该模型能够生成非均匀玻璃散射现象，即使输入数据复杂。