Research on spectral reconstruction algorithm for snapshot microlens array micro-hyperspectral imaging system

Snapshot computed tomographic microscopic imaging spectrometer and its video-level tracking of poisonous Microcystis aeruginosa cells in mixed algae

Conventional microscopic spectral imaging suffers from extended scanning times across wavelength or spatial dimension. To improve capabilities of dynamic microscopic spectral imaging, we developed a snapshot computed tomographic microscopic imaging spectrometer (CTMIS) based on the zeroth and first orders dispersive diffraction of a two-dimensional grating. Utilizing the CTMIS-UNET reconstruction algorithm, we can reconstruct a spectral cube (541x541x26) for each frame of micro spectral imaging video. Experimental results demonstrate a sub-4 μm spatial resolution achievable through a 20x objective lens and a spectral resolution better than 10 nm among 450-700 nm, while maintaining spectral cosine similarities exceeding 0.9989 when comparing reconstructed spectra with ground truth data. Spectral imaging videos of four species of algae and mixed algae were captured under 10 ms exposure time using the CTMIS system. Leveraging the self-developed UNET-SI26 algae recognition network, precise identification and tracking of four types of algae and poisonous microcysts aeruginosa in mixed algae were conducted. The pixel-level recognition accuracy exceeds 95 %, while the accuracy for counting the numbers of different types of cells surpasses 85 %, offering an efficient and accurate spectral imaging method for real-time monitoring and early warning of harmful algae at the cellular level.

Learning based compressive snapshot spectral light field imaging with RGB sensors

The application of multidimensional optical sensing technologies, such as the spectral light field (SLF) imager, has become increasingly common in recent years. The SLF sensors provide information in the form of one-dimensional spectral data, two-dimensional spatial data, and two-dimensional angular measurements. Spatial-spectral and angular data are essential in a variety of fields, from computer vision to microscopy. Beam-splitters or expensive camera arrays are required for the usage of SLF sensors. The paper describes a low-cost RGB light field camera-based compressed snapshot SLF imaging method. Inspired by the compressive sensing paradigm, the four dimensional SLF can be reconstructed from a measurement of an RGB light field camera via a network which is proposed by utilizing a U-shaped neural network with multi-head self-attention and unparameterized Fourier transform modules. This method is capable of gathering images with a spectral resolution of 10 nm, angular resolution of 9 × 9, and spatial resolution of 622 × 432 within the spectral range of 400 to 700 nm. It provides us an alternative approach to implement the low cost SLF imaging.

Spatial-spectral resolution tunable snapshot imaging spectrometer: analytical design and implementation

A snapshot imaging spectrometer is a powerful tool for dynamic target tracking and real-time recognition compared with a scanning imaging spectrometer. However, all the current snapshot spectral imaging techniques suffer from a major trade-off between the spatial and spectral resolutions. In this paper, an integral field snapshot imaging spectrometer (TIF-SIS) with a continuously tunable spatial-spectral resolution and light throughput is proposed and demonstrated. The proposed TIF-SIS is formed by a fore optics, a lenslet array, and a collimated dispersive subsystem. Theoretical analyses indicate that the spatial-spectral resolution and light throughput of the system can be continuously tuned through adjusting the F number of the fore optics, the rotation angle of the lenslet array, or the focal length of the collimating lens. Analytical relationships between the spatial and spectral resolutions and the first-order parameters of the system with different geometric arrangements of the lenslet unit are obtained. An experimental TIF-SIS consisting of a self-fabricated lenslet array with a pixelated scale of 100×100 and a fill factor of 0.716 is built. The experimental results show that the spectral resolution of the system can be steadily improved from 4.17 to 0.82 nm with a data cube (N x×N y×N λ) continuously tuned from 35×35×36 to 40×40×183 in the visible wavelength range from 500 to 650 nm, which is consistent with the theoretical prediction. The proposed method for real-time tuning of the spatial-spectral resolution and light throughput opens new possibilities for broader applications, especially for recognition of things with weak spectral signature and biomedical investigations where a high light throughput and tunable resolution are needed.

Deep ultraviolet high-resolution microscopic hyperspectral imager and its biological tissue detection

Ultraviolet (UV) hyperspectral imaging technology is commonly used in the field of atmospheric remote sensing. In recent years, some in-laboratory research has been carried out for substance detection and identification. In this paper, UV hyperspectral imaging technology is introduced into microscopy to better utilize the obvious absorption characteristics of components, such as proteins and nucleic acids in biological tissues in the ultraviolet band. A deep UV microscopic hyperspectral imager based on the Offner structure with F # 2.5, low spectral keystone and smile is designed and developed. A 0.68 numerical aperture microscope objective is designed. The spectral range of the system is from 200 nm to 430 nm; the spectral resolution is better than 0.5 nm; and the spatial resolution is better than 1.3 µm. The K562 cells can be distinguished by transmission spectrum of nucleus. The UV microscopic hyperspectral image of the unstained mouse liver slices showed similar results to the microscopic image after hematoxylin and eosin staining, which could help to simplify the pathological examination process. Both results show a great performance in spatial and spectral detecting capabilities of our instrument, which has the potential for biomedical research and diagnosis.

Analytical design of a cemented-curved-prism based integral field spectrometer (CIFS) with high numerical aperture and high resolution

Snapshot hyperspectral imaging is superior to scanning spectrometers due to its advantage in dimensionality, allowing longer pixel dwell time and higher data cube acquisition efficiency. Due to the trade-off between spatial and spectral resolution in snapshot spectral imaging technologies, further improvements in the performance of snapshot imaging spectrometers are limited. Therefore, we propose a cemented-curved-prism-based integral field spectrometer (CIFS), which achieves high spatial and high spectral resolution imaging with a high numerical aperture. It consists of a hemispherical lens, a cemented-curved-prism and a concave spherical mirror. The design idea of aplanatic imaging and sharing-optical-path lays the foundation for CIFS to exhibit high-resolution imaging in a compact structure. The numerical model between the parameters of optical elements and the spectral resolution of the system is established, and we analyze the system resolution influenced by the hemispherical lens and the cemented-curved-prism. Thus, the refractive index requirements of the hemispherical lens and the cemented-curved-prism for the optimal spatial and spectral resolution imaging of the system are obtained, providing guidance for the construction of CIFS. The designed CIFS achieves pupil matching with a 1.8 f-number lenslet array, sampling 268 × 76 spatial points with 403 spectral channels in the wavelength band of 400 to 760 nm. The spectral and spatial resolution are further evaluated through a simulation experiment of spectral imaging based on Zemax. It paves the way for developing integral field spectrometers exhibiting high spatial and high spectral resolution imaging with high numerical aperture.

Deep spatial-spectral prior with an adaptive dual attention network for single-pixel hyperspectral reconstruction

Recently, single-pixel imaging has shown great promise in developing cost-effective imaging systems, where coding and reconstruction are the keys to success. However, it also brings challenges in capturing hyperspectral information accurately and instantly. Many works have attempted to improve reconstruction performance in single-pixel hyperspectral imaging by applying various hand-crafted priors, leading to sub-optimal solutions. In this paper, we present the deep spatial-spectral prior with adaptive dual attention network for single-pixel hyperspectral reconstruction. Specifically, the spindle structure of the parameter sharing method is developed to integrate information across spatial and spectral dimensions of HSI, which can synergistically and efficiently extract global and local prior information of hyperspectral images from both shallow and deep layers. Particularly, a sequential adaptive dual attention block (SADAB), i.e., spatial attention and spectral attention, are devised to adaptively rescale informative features of spatial locations and spectral channels simultaneously, which can effectively boost the reconstruction accuracy. Experiment results on public HSI datasets demonstrate that the proposed method significantly outperforms the state-of-the-art algorithm in terms of reconstruction accuracy and speed.