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。