Guozhong Lei, Wenchang Lai, Qi Meng, Wenda Cui, Hao Liu, Yan Wang, Kai Han
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引用次数: 0
摘要
在本手稿中,一种自动优化神经网络被应用于哈达玛单像素成像(H-SPI)和傅立叶单像素成像(F-SPI),以提高低采样率下的成像质量,这种网络被称为 AO-Net。通过将哈达玛或傅里叶基照明光场投射到物体上,使用单像素探测器收集物体的反射光强度。将一维检测值输入设计好的 AO 网络,网络就能自动优化。最后,通过多次迭代输出高质量图像,而无需预先训练和数据集。数值模拟和实验证明,在低采样率的二值图像和灰度图像中,AO-Net 的表现优于其他现有的普遍方法。特别是,当采样率小于 3% 时,二值重建图像的结构相似性指数测量值可以达到 0.95 以上。因此,AO-Net 在复杂环境成像和移动物体成像领域具有巨大的应用潜力。
Low-sampling high-quality Hadamard and Fourier single-pixel imaging through automated optimization neural network
In this manuscript, an automated optimization neural network is applied in Hadamard single-pixel imaging (H-SPI) and Fourier single-pixel imaging (F-SPI) to improve the imaging quality at low sampling ratios which is called AO-Net. By projecting Hadamard or Fourier basis illumination light fields onto the object, a single-pixel detector is used to collect the reflected light intensities from object. The one-dimensional detection values are fed into the designed AO-Net, and the network can automatically optimize. Finally, high-quality images are output through multiple iterations without pre-training and datasets. Numerical simulations and experiments demonstrate that AO-Net outperforms other existing widespread methods for both binary and grayscale images at low sampling ratios. Specially, the Structure Similarity Index Measure value of the binary reconstructed image can reach more than 0.95 when the sampling ratio is less than 3%. Therefore, AO-Net holds great potential for applications in the fields of complex environment imaging and moving object imaging.
期刊介绍:
Frontiers in Physics publishes rigorously peer-reviewed research across the entire field, from experimental, to computational and theoretical physics. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, engineers and the public worldwide.