DeTNet: a projector deblurring method based on CNN and Triplet attention

Yuqiang Zhang; Huamin Yang; Cheng Han; Chao Zhang

doi:10.1117/12.3045197

9 October 2024 DeTNet: a projector deblurring method based on CNN and Triplet attention

Yuqiang Zhang, Huamin Yang, Cheng Han, Chao Zhang

Proceedings Volume 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024); 132880Y (2024) https://doi.org/10.1117/12.3045197
Event: Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 2024, Chengdu, China

Abstract

Projection blurring and defocusing is a prevalent issue that can significantly degrade the quality and legibility of projected images and visual content. This paper introduces a novel method to address this problem through the development of a deblurring network based on convolution operation and Triplet attention (DeTNet). This dual-pronged design enables the network to effectively extract salient features related to out-of-focus blurring, while also capturing the crucial interdependencies and interactions across multiple feature dimensions. By modeling both the low-level blur characteristics as well as the higher-order feature correlations, the DeTNet is able to reconstruct sharper, more focused projection outputs. Through extensive experimental validation on the collected datasets, the effectiveness of the proposed approach is thoroughly demonstrated.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuqiang Zhang, Huamin Yang, Cheng Han, and Chao Zhang "DeTNet: a projector deblurring method based on CNN and Triplet attention", Proc. SPIE 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 132880Y (9 October 2024); https://doi.org/10.1117/12.3045197

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KEYWORDS

Deblurring

Projection systems

Feature extraction

Education and training

Visualization

Network architectures

Image processing

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