Defocus Deblurring Using Dual-Pixel Data

• URL: http://arxiv.org/abs/2005.00305v3
• Date: Thu, 16 Jul 2020 23:49:50 GMT
• Title: Defocus Deblurring Using Dual-Pixel Data
• Authors: Abdullah Abuolaim and Michael S. Brown
• Abstract summary: Defocus blur arises in images that are captured with a shallow depth of field due to the use of a wide aperture. We propose an effective defocus deblurring method that exploits data available on dual-pixel (DP) sensors found on most modern cameras.
• Score: 41.201653787083735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Defocus blur arises in images that are captured with a shallow depth of field due to the use of a wide aperture. Correcting defocus blur is challenging because the blur is spatially varying and difficult to estimate. We propose an effective defocus deblurring method that exploits data available on dual-pixel (DP) sensors found on most modern cameras. DP sensors are used to assist a camera's auto-focus by capturing two sub-aperture views of the scene in a single image shot. The two sub-aperture images are used to calculate the appropriate lens position to focus on a particular scene region and are discarded afterwards. We introduce a deep neural network (DNN) architecture that uses these discarded sub-aperture images to reduce defocus blur. A key contribution of our effort is a carefully captured dataset of 500 scenes (2000 images) where each scene has: (i) an image with defocus blur captured at a large aperture; (ii) the two associated DP sub-aperture views; and (iii) the corresponding all-in-focus image captured with a small aperture. Our proposed DNN produces results that are significantly better than conventional single image methods in terms of both quantitative and perceptual metrics -- all from data that is already available on the camera but ignored. The dataset, code, and trained models are available at https://github.com/Abdullah-Abuolaim/defocus-deblurring-dual-pixel.

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