Yang Xu, Zhang Hui, Zhang Hancui, Wu Long, Xu Lu, Zhang Yong, Yang Zhen
Opt Express. 2024 May 20;32(11):18618-18638. doi: 10.1364/OE.522516.
Fourier single pixel imaging utilizes pre-programmed patterns for laser spatial distribution modulation to reconstruct intensity image of the target through reconstruction algorithms. The approach features non-locality and high anti-interference performance. However, Poor image quality is induced when the target of interest is occluded in Fourier single pixel imaging. To address the problem, a deep learning-based image inpainting algorithm is employed within Fourier single pixel imaging to reconstruct partially obscured targets with high quality. It applies a distance-based segmentation method to segment obscured regions and the target of interest. Additionally, it utilizes an image inpainting network that combines multi-scale sparse convolution and transformer architecture, along with a reconstruction network that integrates Channel Attention Mechanism and Attention Gate modules to reconstruct complete and clear intensity images of the target of interest. The proposed method significantly expands the application scenarios and improves the imaging quality of Fourier single pixel imaging. Simulation and real-world experimental results demonstrate that the proposed method exhibits the high inpainting and reconstruction capacity in the conditions of hard occlusion and down-sampling.
傅里叶单像素成像利用预编程图案对激光空间分布进行调制，通过重建算法重建目标的强度图像。该方法具有非局部性和高抗干扰性能。然而，在傅里叶单像素成像中，当感兴趣的目标被遮挡时，图像质量会变差。为了解决这个问题，在傅里叶单像素成像中采用了基于深度学习的图像修复算法，以高质量地重建部分被遮挡的目标。它应用基于距离的分割方法来分割被遮挡区域和感兴趣的目标。此外，它利用了一个结合多尺度稀疏卷积和变压器架构的图像修复网络，以及一个集成通道注意力机制和注意力门模块的重建网络，以重建感兴趣目标的完整、清晰的强度图像。所提出的方法显著扩展了应用场景，提高了傅里叶单像素成像的成像质量。仿真和实际实验结果表明，该方法在硬遮挡和下采样条件下具有较高的修复和重建能力。
