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In this paper, we tackle the problem of monocular bokeh synthesis, where weattempt to render a shallow depth of field image from a single all-in-focusimage. Unlike in DSLR cameras, this effect can not be captured directly inmobile cameras due to the physical constraints of the mobile aperture. We thuspropose a network-based approach that is capable of rendering realisticmonocular bokeh from single image inputs. To do this, we introduce three newedge-aware Bokeh Losses based on a predicted monocular depth map, that sharpensthe foreground edges while blurring the background. This model is thenfinetuned using an adversarial loss to generate a realistic Bokeh effect.Experimental results show that our approach is capable of generating apleasing, natural Bokeh effect with sharp edges while handling complicatedscenes.

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