Enhanced multi-stage network for defocus deblurring using dual-pixel images

The defocus deblurring raised from the finite aperture size and exposure time is an essential problem in the shooting process, which seriously affects the quality of the images. However, studies based on defocus deblurring in monocular images yielded good results, while those on binocular images are rare. The current methods directly merge the left and right views regardless of their unique features. Objects within the camera’s DoF will not have a difference in phase, while light rays from outside the DoF will have a relative shift that is directly correlated with the amount of defocus blur. In this paper, we firstly proposed an enhanced multi-stage network for defocus deblurring using dual-pixel Images. Taking into account the parallax between the left and right views, the first two stages learn the information of them, respectively, and correct the deviation of the images under the supervision of the ground truth. The third stage consists of EERG and ERGS. It merges with the feature map of the previous stage, so that the left and right views are mutually enhanced, and a good restored image is obtained. ERGS uses the residual block as the basic unit to restore the details of the blurred area while maintaining the clear. Experimental results show that our proposed network can achieve better accuracy than state-of-the-art approaches on the public DPD dataset.