Abstract
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 s on all smartphone chipsets. This approach ranked First in AIM 2020 Rendering Realistic Bokeh Challenge Track 1 & Track 2.
M. Qian—The work was done when Ming Qian was an intern at AiRiA.
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Acknowledgements
This work was supported by the Advance Research Program (31511130301); National Key Research and Development Program (2017YFF0209806), and National Natural Science Foundation of China (No. 61906193; No. 61906195; No. 61702510).
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Qian, M. et al. (2020). BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12537. Springer, Cham. https://doi.org/10.1007/978-3-030-67070-2_14
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