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Conference Paper

Learning to Deblur using Light Field Generated and Real Defocus Images


Chen,  Bin
Computer Graphics, MPI for Informatics, Max Planck Society;

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Ruan, L., Chen, B., Li, J., & Lam, M. (2022). Learning to Deblur using Light Field Generated and Real Defocus Images. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16283-16292). Piscataway, NJ: IEEE. doi:10.1109/CVPR52688.2022.01582.

Cite as: https://hdl.handle.net/21.11116/0000-000C-4354-A
Although considerable progress has been made in semantic scene understanding
under clear weather, it is still a tough problem under adverse weather
conditions, such as dense fog, due to the uncertainty caused by imperfect
observations. Besides, difficulties in collecting and labeling foggy images
hinder the progress of this field. Considering the success in semantic scene
understanding under clear weather, we think it is reasonable to transfer
knowledge learned from clear images to the foggy domain. As such, the problem
becomes to bridge the domain gap between clear images and foggy images. Unlike
previous methods that mainly focus on closing the domain gap caused by fog --
defogging the foggy images or fogging the clear images, we propose to alleviate
the domain gap by considering fog influence and style variation simultaneously.
The motivation is based on our finding that the style-related gap and the
fog-related gap can be divided and closed respectively, by adding an
intermediate domain. Thus, we propose a new pipeline to cumulatively adapt
style, fog and the dual-factor (style and fog). Specifically, we devise a
unified framework to disentangle the style factor and the fog factor
separately, and then the dual-factor from images in different domains.
Furthermore, we collaborate the disentanglement of three factors with a novel
cumulative loss to thoroughly disentangle these three factors. Our method
achieves the state-of-the-art performance on three benchmarks and shows
generalization ability in rainy and snowy scenes.