In this paper, we tackle the problem of monocular bokeh synthesis, where we
attempt to render a shallow depth of field image from a single all-in-focus
image. Unlike in DSLR cameras, this effect can not be captured directly in
mobile cameras due to the physical constraints of the mobile aperture. We thus
propose a network-based approach that is capable of rendering realistic
monocular bokeh from single image inputs. To do this, we introduce three new
edge-aware Bokeh Losses based on a predicted monocular depth map, that sharpens
the foreground edges while blurring the background. This model is then
finetuned using an adversarial loss to generate a realistic Bokeh effect.
Experimental results show that our approach is capable of generating a
pleasing, natural Bokeh effect with sharp edges while handling complicated
scenes.

本研究提出了基于网络的方法，利用预测的单眼深度图和三种新的边缘感知 Bokeh 损失，以生成逼真的单眼 Bokeh 效果，并使用对抗性损失进行微调，实验结果表明，我们的方法能够处理复杂场景，产生锐利边缘而自然的 Bokeh 效果。

Bokeh-Loss GAN：逼真且边缘感知的虚化多阶段对抗训练

Bokeh-Loss GAN: Multi-Stage Adversarial Training for Realistic  Edge-Aware Bokeh

Shallow depth-of-field is commonly used by photographers to isolate a subject
from a distracting background. However, standard cell phone cameras cannot
produce such images optically, as their short focal lengths and small apertures
capture nearly all-in-focus images. We present a system to computationally
synthesize shallow depth-of-field images with a single mobile camera and a
single button press. If the image is of a person, we use a person segmentation
network to separate the person and their accessories from the background. If
available, we also use dense dual-pixel auto-focus hardware, effectively a
2-sample light field with an approximately 1 millimeter baseline, to compute a
dense depth map. These two signals are combined and used to render a defocused
image. Our system can process a 5.4 megapixel image in 4 seconds on a mobile
phone, is fully automatic, and is robust enough to be used by non-experts. The
modular nature of our system allows it to degrade naturally in the absence of a
dual-pixel sensor or a human subject.

该研究提出一种使用单一手机相机和按键合成浅景深图像的系统，如果该图像是一个人，则使用人体分割网络将人和其配件与背景分离，并使用密集二像素自动对焦硬件计算出密集深度图。这两个信号结合起来用于渲染一张非聚焦的图像。