论文标题
使用双像素数据的DeDocus Deblurring
Defocus Deblurring Using Dual-Pixel Data
论文作者
论文摘要
由于使用宽光圈，在用浅深度捕获的图像中出现了defocus模糊。纠正散焦模糊是具有挑战性的，因为模糊在空间上变化且难以估计。我们提出了一种有效的Defocus Deblurring方法，该方法利用在大多数现代相机上发现的双像素（DP）传感器上可用的数据。 DP传感器用于通过单个图像拍摄的场景捕获两个亚曲线视图来帮助相机的自动对焦。两个子孔图图像用于计算适当的镜头位置，以专注于特定场景区域，然后被丢弃。我们介绍了一个深度神经网络（DNN）体系结构，该架构使用这些废弃的子孔径图像来减少散热器的模糊。我们努力的一个关键贡献是精心捕获的500个场景（2000张图像）的数据集，其中每个场景都有：（i）在大量孔径上捕获的带有defocus模糊的图像； （ii）两个相关的DP子孔径视图； （iii）用小光圈捕获的相应的全焦点图像。我们提出的DNN产生的结果在定量和感知指标方面都比传统的单像方法明显好 - 这都是从相机上已经可用但被忽略的数据。数据集，代码和训练的模型可在https://github.com/abdullah-abuolaim/defocus-deblurring-dual像素上找到。
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.
