论文标题
实时玫瑰花特成像仪的性能估计
Performance Estimation of a Real-Time Rosette Imager
论文作者
论文摘要
在本文中，我们对一个实时可行的玫瑰花结构成像仪进行建模，该实时可行的玫瑰花结成像仪，该成像仪由玫瑰花塞扫描仪，光学传感器和确定性图像重建算法组成。我们通过选择适当的视野和玫瑰花结图案来微调玫瑰花结构图像仪。传感器视野是通过使用均匀随机采样的贪婪方法来确定的。此外，通过确定哪种图案最能统一覆盖成像区域来选择最佳的玫瑰花结图案。 我们使用PSNR，峰值信号与噪声比率探索图像稀疏性，图像删除和高斯过滤数据集，并探索死叶数据集。这种探索有助于建立PSNR和图像稀疏之间的联系。此外，我们比较了贝叶斯框架中的各种玫瑰花结构配置。我们还得出结论，在图像质量方面，rosette成像仪并不优于等效样本的焦距平面阵列，但可以符合性能。
In this paper, we model a real-time feasible rosette imager, consisting of a rosette scanner, an optical sensor and a deterministic image reconstruction algorithm. We fine-tune the rosette imager through selecting the appropriate sensor field of view and rosette pattern. The sensor field of view is determined through a greedy approach using uniform random sampling. Furthermore, the optimal rosette pattern is selected by determining which pattern best covers the imaging area uniformly. We explore image sparsity, image decimation and Gaussian filtering in a well-known natural data set and dead leaves data set using the PSNR, Peak-Signal-to-Noise Ratio. This exploration helps to establish a connection between PSNR and image sparsity. Furthermore, we compare various rosette imager configurations in a Bayesian framework. We also conclude that the rosette imager does not outperform a focal-plane array of equivalent samples in terms of image quality but can match the performance.
