The traditional algorithm for compressive reconstruction has high computational complexity. In order to reduce the reconstruction time of compressive sensing, deep learning networks have proven to be an effective solution. In this paper, we have developed a single-pixel imaging system based on deep learning and designed the binary sampling Res2Net reconstruction network (Bsr2-Net) model suitable for binary matrix sampling. In the experiments, we compared the structural similarity, peak signal-to-noise ratio, and reconstruction time using different reconstruction methods. Experimental results show that the Bsr2-Net is superior to several deep learning networks recently reported and closes to the most advanced reconstruction algorithms.

中文翻译：

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用于单像素成像的二进制采样 Res2net 重建网络

传统的压缩重建算法计算复杂度高。为了减少压缩感知的重建时间，深度学习网络已被证明是一种有效的解决方案。在本文中，我们开发了基于深度学习的单像素成像系统，并设计了适用于二进制矩阵采样的二进制采样 Res2Net 重建网络（Bsr2-Net）模型。在实验中，我们比较了使用不同重建方法的结构相似性、峰值信噪比和重建时间。实验结果表明，Bsr2-Net 优于最近报道的几种深度学习网络，并接近最先进的重建算法。