论文标题
基于自适应面具的金字塔网络，用于现实的散景渲染
Adaptive Mask-based Pyramid Network for Realistic Bokeh Rendering
论文作者
论文摘要
散景效应在模糊图像的其余部分时突出显示对象（或图像的任何部分），并产生视觉上令人愉悦的艺术效果。由于基于传感器的移动设备限制，基于机器学习（ML）的散布渲染已成为可靠的替代方案。在本文中，我们专注于基于ML的Bokeh渲染的一些改进； i）具有高分辨率图像的设备性能，ii）能够用用户可用面罩引导散景产生散景和III）能够产生不同的模糊强度的能力。为此，我们提出了基于自适应掩模的金字塔网络（AMPN），该金字塔网络由蒙版引导的散景发电机（MGBG）块和Laplacian金字塔改进（LPR）块形成。 MGBG由两个彼此堆叠以产生散景效应的轻量级网络组成，LPR完善并为MGBG的输出提供了示例，以产生高分辨率的散景图像。我们实现了i）通过轻巧，对移动友好的设计选择，ii）通过MGBG的堆叠网络设计和弱监督的蒙版预测方案以及III）通过手动或自动编辑掩模的强度值来指导Bokeh Generation。除了这些功能外，我们的结果表明，与现有方法相比，AMPN会产生竞争或更好的结果！数据集，虽然比替代方案更快且小。
Bokeh effect highlights an object (or any part of the image) while blurring the rest of the image, and creates a visually pleasant artistic effect. Due to the sensor-based limitations on mobile devices, machine learning (ML) based bokeh rendering has gained attention as a reliable alternative. In this paper, we focus on several improvements in ML-based bokeh rendering; i) on-device performance with high-resolution images, ii) ability to guide bokeh generation with user-editable masks and iii) ability to produce varying blur strength. To this end, we propose Adaptive Mask-based Pyramid Network (AMPN), which is formed of a Mask-Guided Bokeh Generator (MGBG) block and a Laplacian Pyramid Refinement (LPR) block. MGBG consists of two lightweight networks stacked to each other to generate the bokeh effect, and LPR refines and upsamples the output of MGBG to produce the high-resolution bokeh image. We achieve i) via our lightweight, mobile-friendly design choices, ii) via the stacked-network design of MGBG and the weakly-supervised mask prediction scheme and iii) via manually or automatically editing the intensity values of the mask that guide the bokeh generation. In addition to these features, our results show that AMPN produces competitive or better results compared to existing methods on the EBB! dataset, while being faster and smaller than the alternatives.
