ACS Photonics最新文献

Far-field super-resolution fluorescence microscopy has been rapidly developed for applications ranging from cell biology to nanomaterials. However, it remains a significant challenge to achieve super-resolution imaging at depth in opaque materials. In this study, we present a super-resolution microscopy technique for imaging hidden fluorescent objects through scattering media, started by exploiting the inherent object replica generation arising from the memory effect, i.e., the seemingly informationless emission speckle can be regarded as a random superposition of multiple object copies. Inspired by the concept of super-resolution optical fluctuation imaging, we use temporally fluctuating speckles to excite fluorescence signals and perform high-order cumulant analysis on the fluctuation, which can not only improve the image resolution but also increase the speckle contrast to isolate only the bright object replicas. A super-resolved image can be finally retrieved by simply unmixing the sparsely distributed replicas with their location map. This methodology allows one to overcome scattering and achieve robust super-resolution fluorescence imaging, circumventing the need for heavy computational steps.

The growth of ultrafast nanophotonic circuits necessitates the development of energy-efficient on-chip pulse characterization techniques. Nanophotonic realizations of Frequency Resolved Optical Gating (FROG), a common pulse characterization technique in bulk optics, have been challenging due to their noncollinear nature and the lack of efficient nonlinear optical processes in the integrated platform. Here, we experimentally demonstrate a novel FROG-based technique compatible with the nanophotonic platform that leverages the high gain-bandwidth of a dispersion-engineered degenerate optical parametric amplifier (DOPA) for energy-efficient ultrashort pulse characterization. We demonstrate on-chip pulse characterization of sub-80 fs, ∼1 fJ pulses using just ∼60 fJ of gate pulse energy, which is several orders of magnitude lower than the gate pulse energy required for characterizing similar pulses in the bulk counterpart. In the future, we anticipate our work will enable the characterization of ultraweak-ultrashort pulses with energies at the single photon level.

Computer vision tasks require processing large amounts of data to perform image classification, segmentation, and feature extraction. Optical preprocessors can potentially reduce the number of floating-point operations required by computer vision tasks, enabling low-power and low-latency operation. However, existing optical preprocessors are mostly learned and hence strongly depend on the training data and thus lack universal applicability. In this paper, we present a meta-optic imager, which implements the Radon transform, obviating the need for training the optics. High-quality image reconstruction with a large compression ratio of 9.2% is presented through the use of the simultaneous algebraic reconstruction technique. We also demonstrate image classification with 90% accuracy on a further compressed (0.6% of total measured pixels) Radon data set through a neural network trained on digitally transformed images. Our work shows the efficacy of data-independent encoding in an optical encoder. While our platform is based on meta-optics, we note that such encoding can be performed with other optics as well.