Compressed sensing is a theory which can reconstruct an image almost perfectly with only a few measurements by finding its sparsest representation. However, the computation time consumed for large images may be a few hours or more. In this work, we both theoretically and experimentally demonstrate a method that combines the advantages of both adaptive computational ghost imaging and compressed sensing, which we call adaptive compressive ghost imaging, whereby both the reconstruction time and measurements required for any image size can be significantly reduced. The technique can be used to improve the performance of all computational ghost imaging protocols, especially when measuring ultra-weak or noisy signals, and can be extended to imaging applications at any wavelength.
High-speed secure key distribution over an optical network based on computational correlation imaging
Protocol based on compressed sensing for high-speed authentication and cryptographic key distribution over a multiparty optical network
Cryptographic key distribution over a public network via variance-based watermarking in compressive measurements
Adaptive compressed photon counting 3D imaging based on wavelet trees and depth map sparse representation
Ghost imaging utilizing experimentally acquired degree of linear polarization with no prior information
Performance analysis of compressive ghost imaging based on different signal reconstruction techniques
Evaluation criterion of thermal light ghost imaging based on the receiver operating characteristic analysis
Cell Imaging in CNS
Here is the latest research on cell imaging and imaging modalities, including light-sheet microscopy, in the central nervous system.