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