Abdullah-Abuolaim / multi-task-defocus-deblurring-dual-pixel-nimat

Reference github repository for the paper "Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning". We propose a single-image deblurring network that incorporates the two sub-aperture views into a multitask framework. Specifically, we show that jointly learning to predict the two DP views from a single blurry input image improves the network’s ability to learn to deblur the image. Our experiments show this multi-task strategy achieves +1dB PSNR improvement over state-of-the-art defocus deblurring methods. In addition, our multi-task framework allows accurate DP-view synthesis (e.g., ~ 39dB PSNR) from the single input image. These high-quality DP views can be used for other DP-based applications, such as reflection removal. As part of this effort, we have captured a new dataset of 7,059 high-quality images to support our training for the DP-view synthesis task.

Date Created 2021-08-11 (3 years ago)

Commits 16 (last one 2 years ago)

Stargazers 46 (0 this week)

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License apache-2.0

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

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updated: 2024-11-07 @ 07:52pm, id: 394848129 / R_kgDOF4jngQ