Congress 1724
  • Saqib Nazir, Lorenzo Vaquero, Manuel Mucientes, Víctor M. Brea, Daniela Coltuc
  • IEEE International Conference on Image Processing (ICIP 2022). Burdeos, Francia. 2022

2HDED:NET for joint depth estimation and image deblurring from a single out-of-focus image

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.
Keywords: Depth from Defocus, Image Deblurring, Deep Learning
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