ProDebNet: projector deblurring using a convolutional neural network

Optics Express
Yuta KageyamaKosuke Sato

Abstract

Projection blur can occur in practical use cases that have non-planar and/or multi-projection display surfaces with various scattering characteristics because the surface often causes defocus and subsurface scattering. To address this issue, we propose ProDebNet, an end-to-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.

References

Sep 21, 2004·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Zhou WangEero P Simoncelli
Mar 8, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Paolo Favaro, Stefano Soatto
Jan 14, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Chao DongXiaoou Tang
Jul 23, 2016·IEEE Transactions on Visualization and Computer Graphics·Gaku NaritaMasatoshi Ishikawa

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