Defocus Deblurring Using Dual-Pixel Data
This addresses image quality issues for photographers and camera users, offering an incremental improvement by leveraging existing but unused sensor data.
The paper tackles defocus blur in images by using dual-pixel sensor data, proposing a deep neural network that significantly outperforms conventional single-image methods in quantitative and perceptual metrics.
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.