论文标题
次波长显微镜的远场强度特征
Far-field intensity signature of sub-wavelength microscopic objects
论文作者
论文摘要
有关具有小于衍射极限的特征的微观对象的信息几乎完全丢失在远场衍射图像中，但可以通过数据完成技术部分恢复。任何此类方法在很大程度上取决于噪声水平。最近，通过使用压缩感测和机器学习来研究了这一新的跨分辨率途径。我们演示了一种基于反卷积和遗传优化的两阶段技术，该技术可以恢复具有波长1/10特征的对象。我们指出，与基于L2的对应物相比，与稀疏性无关的傅立叶域中的基于L1 - 总体的优化对噪声更强大。我们还为基于稀疏数据运行的基于傅立叶变换的迭代算法引入了非常快速的通用限制域计算方法。
Information about microscopic objects with features smaller than the diffraction limit is almost entirely lost in a far-field diffraction image but could be partly recovered with data completition techniques. Any such approach critically depends on the level of noise. This new path to superresolution has been recently investigated with the use of compressed sensing and machine learning. We demonstrate a two-stage technique based on deconvolution and genetic optimization which enables the recovery of objects with features of 1/10 of the wavelength. We indicate that l1-norm based optimization in the Fourier domain unrelated to sparsity is more robust to noise than its l2-based counterpart. We also introduce an extremely fast general-purpose restricted domain calculation method for Fourier transform based iterative algorithms operating on sparse data.
