We present a method that takes as input a single dual-pixel image, and simultaneously estimates the image's defocus map -- the amount of defocus blur at each pixel -- and recovers an all-in-focus image. Our method is inspired from recent works that leverage the dual-pixel sensors available in many consumer cameras to assist with autofocus, and use them for recovery of defocus maps or all-in-focus images. These prior works have solved the two recovery problems independently of each other, and often require large labeled datasets for supervised training. By contrast, we show that it is beneficial to treat these two closely-connected problems simultaneously. To this end, we set up an optimization problem that, by carefully modeling the optics of dual-pixel images, jointly solves both problems. We use data captured with a consumer smartphone camera to demonstrate that, after a one-time calibration step, our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised.

翻译：我们提出一种方法,将单一的双像素图像作为输入,同时估计图像的脱焦图 -- -- 每个像素的脱焦量 -- -- 并恢复一个全焦图像。我们的方法来自最近的工作,利用许多消费相机中可用的双像素传感器协助自动聚焦,并用它们来恢复脱焦地图或全部在焦图像。这些以前的工作分别解决了两个恢复问题,并经常需要大标记的数据集来进行监管培训。相反,我们表明同时处理这两个密切相关的问题是有益的。为此,我们设置了一个优化问题,通过仔细模拟双像素图像的光学模型,共同解决这两个问题。我们用一个消费智能手机相机捕捉到的数据来显示,在一次性校准步骤之后,我们的方法改进了先前的脱焦图估测和模糊清除工作,尽管完全没有被超超。