HRPD: a lightweight high-resolution projector deblurring network
Yuqiang Zhang, Huamin Yang, Cheng Han, Chao Zhang · 2024
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.