Rapid Image Reconstruction of Structured Illumination Microscopy Directly in the Spatial Domain

Parameter-free super-resolution structured illumination microscopy via a physics-enhanced neural network

With full-field imaging and high photon efficiency advantages, structured illumination microscopy (SIM) is one of the most potent super-resolution (SR) modalities in bioscience. Regarding SR reconstruction for SIM, spatial domain reconstruction (SDR) has been proven to be faster than traditional frequency domain reconstruction (FDR), facilitating real-time imaging of live cells. Nevertheless, SDR relies on high-precision parameter estimation for reconstruction, which tends to suffer from low signal-to-noise ratio (SNR) conditions and inevitably leads to artifacts that seriously affect the accuracy of SR reconstruction. In this Letter, a physics-enhanced neural network-based parameter-free SDR (PNNP-SDR) is proposed, which can achieve SR reconstruction directly in the spatial domain. As a result, the peak-SNR (PSNR) of PNNP-SDR is improved by about 4 dB compared to the cross-correlation (COR) SR reconstruction; meanwhile, the reconstruction speed of PNNP-SDR is even about five times faster than the fast approach based on principal component analysis (PCA). Given its capability of achieving parameter-free imaging, noise robustness, and high-fidelity and high-speed SR reconstruction over conventional SIM microscope hardware, the proposed PNNP-SDR is expected to be widely adopted in biomedical SR imaging scenarios.

Snapshot compressive structured illumination microscopy

We propose a snapshot compressive structured illumination microscopy (SoSIM) system to increase the number of reconstructed resolution-enhanced (RE) images per second and reduce the data bandwidth by capturing compressed measurements. In this system, multiple low-resolution images are encoded by a high-speed digital micro-mirror device with random binary masks. These images are then captured by a low-speed camera as a snapshot compressed measurement. Following this, we adopt an efficient deep neural network to reconstruct nine images with different structured illumination patterns from a single measurement. The reconstructed images are then combined into a single-frame RE image using the method of spectral synthesis in the frequency domain. When the camera operates at 100 frames per second (fps), we can eventually recover dynamic RE videos at the same speed with 100 fps.

Superresolution structured illumination microscopy reconstruction algorithms: a review

Structured illumination microscopy (SIM) has become the standard for next-generation wide-field microscopy, offering ultrahigh imaging speed, superresolution, a large field-of-view, and long-term imaging. Over the past decade, SIM hardware and software have flourished, leading to successful applications in various biological questions. However, unlocking the full potential of SIM system hardware requires the development of advanced reconstruction algorithms. Here, we introduce the basic theory of two SIM algorithms, namely, optical sectioning SIM (OS-SIM) and superresolution SIM (SR-SIM), and summarize their implementation modalities. We then provide a brief overview of existing OS-SIM processing algorithms and review the development of SR-SIM reconstruction algorithms, focusing primarily on 2D-SIM, 3D-SIM, and blind-SIM. To showcase the state-of-the-art development of SIM systems and assist users in selecting a commercial SIM system for a specific application, we compare the features of representative off-the-shelf SIM systems. Finally, we provide perspectives on the potential future developments of SIM.

Reconstruction algorithm using 2N+1 raw images for structured illumination microscopy

This paper presents a structured illumination microscopy (SIM) reconstruction algorithm that allows the reconstruction of super-resolved images with 2N + 1 raw intensity images, with N being the number of structured illumination directions used. The intensity images are recorded after using a 2D grating for the projection fringe and a spatial light modulator to select two orthogonal fringe orientations and perform phase shifting. Super-resolution images can be reconstructed from the five intensity images, enhancing the imaging speed and reducing the photobleaching by 17%, compared to conventional two-direction and three-step phase-shifting SIM. We believe the proposed technique will be further developed and widely applied in many fields.

Flexible Image Reconstruction in the Orbital Angular Momentum Holography with Binarized Airy Lens

The orbital angular momentum (OAM) holography has been marked a path to achieving ultrahigh capacity holographic information systems. However, the practical applicability of the OAM holography is limited by the complicated optical setup and unadjustable image intensity and position. Here, a decoding method is proposed by using a binarized phase map derived from an autofocusing Airy beam. By adjusting the parameters of the phase map, the position and intensity distribution of the reconstructed image become flexibly adjustable. In addition, the cross-talk between different image channels can be effectively reduced thanks to the abruptly autofocusing capability of the Airy beams. As a result, the quality and practicability of the OAM holography can be greatly enhanced.

High-speed super-resolution imaging with compressive imaging-based structured illumination microscopy

Structured illumination microscopy (SIM) has been widely applied to investigating fine structures of biological samples by breaking the optical diffraction limitation. So far, video-rate imaging has been obtained in SIM, but the imaging speed was still limited due to the reconstruction of a super-solution image through multi-sampling, which hindered the applications in high-speed biomedical imaging. To overcome this limitation, here we develop compressive imaging-based structured illumination microscopy (CISIM) by synergizing SIM and compressive sensing (CS). Compared with conventional SIM, CISIM can greatly improve the super-resolution imaging speed by extracting multiple super-resolution images from one compressed image. Based on CISIM, we successfully reconstruct the super-resolution images in biological dynamics, and analyze the effect factors of image reconstruction quality, which verify the feasibility of CISIM. CISIM paves a way for high-speed super-resolution imaging, which may bring technological breakthroughs and significant applications in biomedical imaging.

The Development of Microscopy for Super-Resolution: Confocal Microscopy, and Image Scanning Microscopy

Optical methods of super-resolution microscopy, such as confocal microscopy, structured illumination, nonlinear microscopy, and image scanning microscopy are reviewed. These methods avoid strong invasive interaction with a sample, allowing the observation of delicate biological samples. The meaning of resolution and the basic principles and different approaches to superresolution are discussed.