2HDED:NET for Joint Depth Estimation and Image Deblurring from a Single Out-of-Focus Image

Saqib Nazir; Lorenzo Vaquero; Manuel Mucientes; Víctor M. Brea; Daniela Coltuc

doi:10.1109/ICIP46576.2022.9897352

2HDED:NET for Joint Depth Estimation and Image Deblurring from a Single Out-of-Focus Image

Saqib Nazir, Lorenzo Vaquero, Manuel Mucientes, Víctor M. Brea, Daniela Coltuc

IEEE International Conference on Image Processing

2HDED:NET performs depth-from-defocus and all-in-focus image restoration in parallel through a shared encoder and two balanced decoder branches.

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Depth estimation and all-in-focus image restoration from defocused RGB images are related problems, although most existing methods address them separately. The few approaches that solve both problems use a pipeline to derive a depth or defocus map as an intermediate product that supports image deblurring. We propose 2HDED:NET, an encoder-decoder network for depth from defocus that is extended with a deblurring branch sharing the same encoder. The two branches operate in parallel and attach equal importance to depth estimation and image restoration. The network is tested on NYU-Depth V2 and compared with state-of-the-art methods for both depth estimation and image deblurring.

@inproceedings{nazir2022twohded,
  author    = {Saqib Nazir and
               Lorenzo Vaquero and
               Manuel Mucientes and
               V{\'{\i}}ctor M. Brea and
               Daniela Coltuc},
  title     = {{2HDED}:{NET} for Joint Depth Estimation and Image Deblurring
               from a Single Out-of-Focus Image},
  booktitle = {{IEEE} Int. Conf. Image Process. ({ICIP})},
  pages     = {2006-2010},
  year      = {2022},
  doi       = {10.1109/ICIP46576.2022.9897352}
}