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.

翻译：Bokeh 效果突出显示一个对象(或图像的任何部分),同时模糊图像的其余部分,并创造视觉上令人愉快的艺术效果。由于基于感官的移动设备限制,机器学习(ML)基于bokeh 的图像已作为一个可靠的替代方案受到关注。在本文中,我们侧重于ML基于bokeh 的布基图像的若干改进;i) 高分辨率图像的安装性能;ii) 以用户版面罩引导bokeh 生成的能力,以及iii) 产生不同模糊强度的能力。为此,我们建议采用基于调适的面具金字塔网络(AMPN)网络(AMPN),这是由Mask-Guid Bokeh 发电机(MG) 区块和 Laplacecian Pyramid Refinement (LPR) 区块组成的。MGBG由两个轻质网络组成,它们相互叠叠合,以产生bokeh 效果,LGBG 改进和增装MGBG 生成更低分辨率的图像。我们通过智能、移动友好的模版版版版的模型,然后通过M-BEM-BEmbreal-dal 生成的模型的模型进行更精确的计算,然后通过这些更精确的模型的生成的模型的模型的生成的模型的模型的模型的生成的模型的生成数据。