  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.
