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29 August 2024 HRPD: a lightweight high-resolution projector deblurring network
Yuqiang Zhang, Huamin Yang, Cheng Han, Chao Zhang
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Proceedings Volume 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024); 132490D (2024) https://doi.org/10.1117/12.3042549
Event: 2024 International Conference on Computer Vision, Robotics and Automation Engineering, 2024, Kunming, China
Abstract
High-resolution projection systems often suffer from blurring artifacts that degrade visual quality. To address this challenge, we propose a novel, lightweight method for high-resolution projection deblurring. Our approach involves developing a compact network architecture by replacing standard convolution layers with depthwise separable convolutions. This substitution significantly reduces the model size and computational complexity, making it suitable for resource-constrained devices. Additionally, we integrated a Triplet attention module into the network to enhance crossdimensional feature interactions. This integration enables the model to better capture and utilize cross-dimension information, resulting in improved deblurring performance. Compared to baseline networks using standard convolutions, our method with depthwise separable convolutions and Triplet attention achieves superior deblurring results, as demonstrated by various evaluation metrics.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yuqiang Zhang, Huamin Yang, Cheng Han, and Chao Zhang "HRPD: a lightweight high-resolution projector deblurring network", Proc. SPIE 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024), 132490D (29 August 2024); https://doi.org/10.1117/12.3042549
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KEYWORDS
Convolution
Deblurring
Projection systems
Image sharpness
Education and training
Image quality
Network architectures
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