  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.
