ProDebNet: Projector deblurring using a convolutional neural network

Yuta Kageyama, Mariko Isogawa, Daisuke Iwai, Kosuke Sato

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Projection blur can occur in practical use cases that have non-planar and/or multiprojection display surfaces with various scattering characteristics because the surface often causes defocus and subsurface scattering. To address this issue, we propose ProDebNet, an endto-end real-time projection deblurring network that synthesizes a projection image to minimize projection blur. The proposed method generates a projection image without explicitly estimating any geometry or scattering characteristics of the projection screen, which makes real-time processing possible. In addition, ProDebNet does not require real captured images for training data; we design a "pseudo-projected"synthetic dataset that is well-generalized to real-world blur data. Experimental results demonstrate that the proposed ProDebNet compensates for two dominant types of projection blur, i.e., defocus blur and subsurface blur, significantly faster than the baseline method, even in a real-projection scene.

Original languageEnglish
Pages (from-to)20391-20403
Number of pages13
JournalOptics Express
Issue number14
Publication statusPublished - 2020 Jul 6
Externally publishedYes

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics


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