用于光子计数单像素成像的去块状采样网络

Deblocking sampling network for photon counting single-pixel imaging

将光子计数技术和单像素成像结合,能实现高灵敏、低成本的光子计数成像,但存在采样时间和重建时间长的问题。基于深度学习的压缩采样和重建网络,将去除偏置和激活函数的全连接层作为测量矩阵,通过从数据中学得高效的测量矩阵和避免传统迭代算法带来的巨大计算量,实现了更快、更高质量的图像重建。但利用全连接层进行高分辨图像的分块压缩感知时,重建图像会产生块状效应。针对该问题提出了重叠分块采样网络(Os_et)、嵌套采样网络(Ns_et)、卷积采样网络(Cs_et)等三种方法以取代全连接层采样。在重建网络的设计中,使用线性映射网络对图像进行重建,设计实验结果表明Cs_et的去块状化效果最好。将Cs_et二值化后应用于光子计数单像素成像系统,实验结果表明Cs_et除块状化明显优于传统算法TVAL3,且Cs_et在重建质量上也同样取得了较好的效果。

Combining photon counting technology with single-pixel imaging can achieve highly sensitive and low cost photon counting imaging, but there are problems of long sampling time and long reconstruction time.The compressed sampling and reconstruction network, which is based on deep learning, uses the fully connected layer without the offset and activation function as the measurement matrix, achieves faster and higher quality image reconstruction by learning efficient measurement matrices from the data and avoids the huge amount of calculation caused by traditional iterative algorithms. However, when the fully connected layer is used for block compression sensing of high-resolution images, the reconstructed image will produce block artifact. In response to this problem, overlapping block sampling network(Os_net), nested sampling network(Ns_net), and convolution sampling network(Cs_net) were proposed: to replace fully connected layer sampling. In the design of the reconstructed network, the images were reconstructed by using a linear mapping network. The design experiment shows that Cs_net has the best deblocking effect. After Cs_net binarization is applied to a photon counting singlepixel imaging system, the experiment results show that Cs_net de-blocking effect is significantly better than the traditional algorithm TVAL3, and Cs_net has also achieved good results on the reconstruction quality.