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. Despite 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) networks 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项基本任务，因其在理解三维场景中的关键作用而备受关注。仅依赖单张图像执行其中任一任务都是不适定问题。深度卷积神经网络领域的最新进展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恢复成为相互关联的问题。尽管如此，大多数现有模型仍将它们分开处理。然而，近期有模型通过顺序串联两个网络，首先估计深度或散焦图，然后基于此重建聚焦图像，从而同时解决这些问题。我们提出了一种并行解决深度估计和图像去模糊的深度神经网络。我们的双头深度估计与去模糊网络（2HDED:NET）扩展了传统的散焦深度估计网络，增加了一个与深度分支共享相同编码器的去模糊分支。该方法已在两个基准测试（面向室内场景的NYU-v2和面向室外场景的Make3D）上成功验证。使用2HDED:NET在这些基准上进行的广泛实验表明，其在深度估计和图像去模糊方面均达到或接近现有最优模型的性能水平。