Fast Random Optical Reconstruction Imaging System and Method Based on Sparse Constraints

Abstract

A fast random optical reconstruction imaging system and method based on sparse constraints, the system at least includes an excitation light source module for generating polychromatic excitation light; fiber coupling and full-scale excitation light for forming collimated excitation light after receiving the polychromatic excitation light. an internal reflection module; an integrated module for imaging, scanning, spatial modulation and detection of the collimated excitation light; a sequential motor control module for controlling the excitation light source module, the optical fiber coupling and total internal reflection module, and the integrated module; The image acquisition and analysis software package performs stochastic reconstruction imaging processing and compressed sensing processing. The invention perfectly satisfies the incoherence between the measurement domain and the image domain, so that the sparse constraint technology can exert the maximum effect. While achieving nanoscale imaging resolution, the existing super-resolution imaging speed has been improved by orders of magnitude. In the case of maintaining the original imaging speed, the spatial resolution has been greatly improved.

Description

technical field [0001] The invention relates to the technical field of super-resolution imaging, in particular to a sparse constraint-based fast stochastic optical reconstruction imaging (STORM) device. Background technique [0002] The development of today's nanotechnology is inseparable from nanomicroscopic imaging technology. In this regard, electron scanning microscopes and atomic force microscopes have become standard configurations in many laboratories. However, optical detection technology has a strong advantage in studying microscopic internal states such as electronic structures, phonon states, and plasmons at the nanoscale, but limited by the optical diffraction limit, its imaging resolution cannot be less than λ / ( 2NA), where λ is the imaging light wavelength, and NA is the numerical aperture of the lens, so that the imaging resolution is generally greater than 180 nanometers, and the microscopic resolution is difficult to improve. To this end, researchers have c...

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Problems solved by technology

The compressive sensing algorithm has been found to have great potential for improving the sampling efficiency of wide-field super-resolution imaging technology. Several research groups, including Xiaowei Zhuang's research group who proposed the STORM technology, have also begun to study the use of different compressive sensing algorithms. From the perspective of signal processing, the sampling time of the above-mentioned optical super-resolution imaging technology is further shortened, but the measurement matrices used in it are all quasi-random matrices, which cannot give full play to the compression characteristics of sparse constraints.

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