论文标题
Bokeh-Loss Gan：对现实边缘感知的散景的多阶段对抗训练
Bokeh-Loss GAN: Multi-Stage Adversarial Training for Realistic Edge-Aware Bokeh
论文作者
论文摘要
在本文中，我们解决了单眼散景合成的问题，在那里我们试图从单个全焦点图像中呈现浅深度图像。与DSLR摄像机不同，由于移动光圈的物理约束，这种效果无法直接在移动摄像机中捕获。因此，我们提出了一种基于网络的方法，该方法能够从单个图像输入中渲染现实的单眼散景。为此，我们根据预测的单眼深度图引入了三个新的边缘感知的散景损失，从而在模糊背景时锐化了前景边缘。然后，使用对抗性损失对该模型进行固定，从而产生逼真的散景效果。实验结果表明，我们的方法能够在处理复杂场景的同时产生令人愉悦的自然景象，并具有锋利的边缘。
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.
