共被期刊论文引用了2次
您的检索式：您选中1篇文献正在查看引证文献汇总
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | 残差注意力聚合对偶回归网络超分辨率计算机断层扫描重建显示文摘为了改善计算机断层扫描(CT)影像重建质量不高的问题,提出一种基于残差注意力聚合对偶回归网络(RAADRNet)的超分辨率CT重建方法。多特征下采样提取模块(MFDEB)通过平均池化、最大池化和卷积运算完成多特征下采样提取,在多特征融合后嵌入通道学习注意力(CLA)和空间学习注意力(SLA),同时并入前级融合特征提取图像的浅层特征。CLA、SLA分别引入通道权重特征学习以及激活函数1+tanh()完成特征提取。残差注意力聚合模块(RAAB)通过CLA嵌入残差网络构成的残差通道学习注意力模块(RCLAB)与SLA构成的空间特征融合模块(SFFB)联合提取图像的深层特征。原始网络在浅层特征与通过亚像素卷积放大的深层特征进行特征融合后完成重建。对偶网络进一步约束重建映射函数的解空间。实验表明,所提算法在重建图像的峰值信噪比(PSNR)和结构相似度(SSIM)上都得到了较好的提升。 | 范金河 吴静 何茂林 | 2023 | 激光与光电子学进展2023，60，2： | 0 |
| 2 | Single-shot real-time compressed ultrahigh-speed imaging enabled by a snapshot-to-video autoencoder显示文摘Single-shot 2 D optical imaging of transient scenes is indispensable for numerous areas of study.Among existing techniques,compressed optical-streaking ultrahigh-speed photography(COSUP)uses a cost-efficient design to endow ultrahigh frame rates with off-the-shelf CCD and CMOS cameras.Thus far,COSUP’s application scope is limited by the long processing time and unstable image quality in existing analytical-modeling-based video reconstruction.To overcome these problems,we have developed a snapshot-to-video autoencoder(S2 V-AE)—which is a deep neural network that maps a compressively recorded 2 D image to a movie.The S2 V-AE preserves spatiotemporal coherence in reconstructed videos and presents a flexible structure to tolerate changes in input data.Implemented in compressed ultrahigh-speed imaging,the S2 V-AE enables the development of single-shot machine-learning assisted real-time(SMART)COSUP,which features a reconstruction time of 60 ms and a large sequence depth of 100 frames.SMART-COSUP is applied to wide-field multiple-particle tracking at 20,000 frames per second.As a universal computational framework,the S2 V-AE is readily adaptable to other modalities in high-dimensional compressed sensing.SMART-COSUP is also expected to find wide applications in applied and fundamental sciences. | XIANGLEI LIU JOÃO MONTEIRO ISABELA ALBUQUERQUE YINGMING LAI CHENG JIANG SHIAN ZHANG TIAGO H.FALK JINYANG LIANG | 2021 | Photonics Research2021，9，12： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员