论文标题
bggan：玻璃玻璃生成对抗网络，用于渲染现实的散景
BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh
论文作者
论文摘要
带有散景效果的照片通常意味着焦点中的物体是锋利的，而异常区域都变得模糊。数码单反相机可以自然地呈现这种效果。但是，由于传感器的限制，智能手机无法直接捕获具有深度效果的图像。在本文中，我们提出了一个名为Glass-Net的新颖发电机，该发电机生成的散景图像不依赖复杂的硬件。同时，基于GAN的方法和感知损失是合并的，以在鉴定模型的阶段呈现现实的散景效果。此外，实例归一化（IN）在我们的网络中重新实现，这确保了我们的Tflite模型IN可以在智能手机GPU上加速。实验表明，我们的方法能够在所有智能手机芯片组的1.9秒内呈现高质量的散景效果并处理一个$ 1024 \ times 1536 $像素图像。这种方法在AIM 2020中排名第一，渲染了现实的散景挑战赛1 \＆Track 2。
A photo captured with bokeh effect often means objects in focus are sharp while the out-of-focus areas are all blurred. DSLR can easily render this kind of effect naturally. However, due to the limitation of sensors, smartphones cannot capture images with depth-of-field effects directly. In this paper, we propose a novel generator called Glass-Net, which generates bokeh images not relying on complex hardware. Meanwhile, the GAN-based method and perceptual loss are combined for rendering a realistic bokeh effect in the stage of finetuning the model. Moreover, Instance Normalization(IN) is reimplemented in our network, which ensures our tflite model with IN can be accelerated on smartphone GPU. Experiments show that our method is able to render a high-quality bokeh effect and process one $1024 \times 1536$ pixel image in 1.9 seconds on all smartphone chipsets. This approach ranked First in AIM 2020 Rendering Realistic Bokeh Challenge Track 1 \& Track 2.
