RT Journal Article
T1 2HDED:NET for joint depth estimation and image deblurring from a single out-of-focus image
A1 Nazir, Saqib
A1 Vaquero Otal, Lorenzo
A1 Mucientes Molina, Manuel
A1 Brea Sánchez, Víctor Manuel
A1 Coltuc, Daniela
K1 Depth from Defocus
K1 Image Deblurring
K1 Deep Learning
AB Depth estimation and all-in-focus image restoration from defocused RGB images are related problems, although most of the existing methods address them separately. The few approaches that solve both problems use a pipeline processing to derive a depth or defocus map as an intermediary product that serves as a support for image deblurring, which remains the primary goal. In this paper, we propose a new Deep Neural Network (DNN) architecture that performs in parallel the tasks of depth estimation and image deblurring, by attaching them the same importance. Our Two-headed Depth Estimation and Deblurring Network (2HDED:NET) is an encoderdecoder network for Depth from Defocus (DFD) that is extended with a deblurring branch, sharing the same encoder. The network is tested on NYU-Depth V2 dataset and compared with several state-of-the-art methods for depth estimation and image deblurring.
PB IEEE
YR 2022
FD 2022-10-18
LK https://hdl.handle.net/10347/43658
UL https://hdl.handle.net/10347/43658
LA eng
NO S. Nazir, L. Vaquero, M. Mucientes, V. M. Brea and D. Coltuc, "2HDED:Net for Joint Depth Estimation and Image Deblurring from a Single Out-of-Focus Image," 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 2022, pp. 2006-2010, doi: 10.1109/ICIP46576.2022.9897352
NO © 2020 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
NO This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 860370. The last author acknowledges financial support from UEFISCDI Romania grant 31/01.01.2021 PN III, 3.6 Suport.
DS Minerva
RD 24 abr 2026