Monocular depth estimation and image deblurring are two fundamental tasks in computer vision, given their crucial role in understanding 3D scenes. Performing any of them by relying on a single image is an ill-posed problem. The recent advances in the field of deep convolutional neural networks (DNNs) have revolutionized many tasks in computer vision, including depth estimation and image deblurring. When it comes to using defocused images, the depth estimation and the recovery of the All-in-Focus (Aif) image become related problems due to defocus physics. In spite of this, most of the existing models treat them separately. There are, however, recent models that solve these problems simultaneously by concatenating two networks in a sequence to first estimate the depth or defocus map and then reconstruct the focused image based on it. We propose a DNN that solves the depth estimation and image deblurring in parallel. Our Two-headed Depth Estimation and Deblurring Network (2HDED:NET) extends a conventional Depth from Defocus (DFD) network with a deblurring branch that shares the same encoder as the depth branch. The proposed method has been successfully tested on two benchmarks, one for indoor and the other for outdoor scenes: NYU-v2 and Make3D. Extensive experiments with 2HDED:NET on these benchmarks have demonstrated superior or close performances to those of the state-of-the-art models for depth estimation and image deblurring.

翻译：单目深度估计和图像去模糊是计算机视觉中的两个基本任务，因为它们在理解三维场景中起着关键作用。依赖单张图像完成其中任何一项任务都是一个不适定问题。深度卷积神经网络领域的近期进展已彻底改变了计算机视觉中的许多任务，包括深度估计和图像去模糊。当涉及使用散焦图像时，由于散焦物理机制，深度估计和全聚焦图像的恢复成为相互关联的问题。尽管如此，现有的大多数模型仍将它们分开处理。然而，近期出现了一些模型通过按顺序连接两个网络来同时解决这些问题：首先估计深度或散焦图，然后基于此重建聚焦图像。我们提出了一种并行解决深度估计和图像去模糊问题的深度神经网络。我们的双头深度估计与去模糊网络扩展了传统的基于散焦的深度估计网络，增加了一个与深度分支共享同一编码器的去模糊分支。所提方法已在两个基准数据集上成功测试，分别针对室内和室外场景：NYU-v2和Make3D。在这些基准上使用2HDED:NET进行的大量实验表明，其在深度估计和图像去模糊方面性能优于或接近最先进模型。