Abdullah-Abuolaim / recurrent-defocus-deblurring-synth-dual-pixel

Reference github repository for the paper "Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data". We propose a procedure to generate realistic DP data synthetically. Our synthesis approach mimics the optical image formation found on DP sensors and can be applied to virtual scenes rendered with standard computer software. Leveraging these realistic synthetic DP images, we introduce a new recurrent convolutional network (RCN) architecture that can improve defocus deblurring results and is suitable for use with single-frame and multi-frame data captured by DP sensors.

Date Created 2020-12-04 (4 years ago)

Commits 13 (last one 2 years ago)

Stargazers 49 (0 this week)

Watchers 5 (0 this week)

Forks 9

License apache-2.0

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dataset datasets autofocus dual-pixel deeplearning defocus-blur deep-learning depth-of-field synthetic-data computer-vision defocus-deblurring deep-neural-networks computational-photography recurrent-neural-networks synthetic-dataset-generation

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updated: 2024-11-06 @ 07:21pm, id: 318603009 / R_kgDOEv1_AQ