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

Brian Lee; Fei Lei; Huaijin Chen; Alexis Baudron

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arXiv · 2208.12343

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

Brian Lee·Fei Lei·Huaijin Chen·Alexis Baudron

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Abstract

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.

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BibTeX RIS

Brian Lee, Fei Lei, Huaijin Chen, Alexis Baudron. 2022-08-25. Bokeh-Loss GAN: Multi-Stage Adversarial Training for Realistic Edge-Aware Bokeh. https://arxiv.org/abs/2208.12343

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