12 results about "Structured illumination microscopy" patented technology

The application provides a motion-resistant structured illumination microscopy method based on principal component analysis (mrPCA-SIM), which can effectively compensate for non-uniform pixel offset and phase error in original illumination images. Compared with the traditional method, the application can realize more stable imaging quality under complex and unstable conditions, and is expected to realize more compatible and flexible live cell super-resolution imaging.

The application discloses self-supervised structured light illumination super-resolution microscopic image denoising, comprising: providing a denoising network with a super-resolution reconstruction module and a super-resolution denoising module, the super-resolution reconstruction module is configured to perform super-resolution reconstruction on an original image to output a super-resolution reconstructed image, the super-resolution denoising module is configured to perform denoising processing on the super-resolution reconstructed image to output a super-resolution denoised image, the super-resolution denoising module is built by using a neural network, and the denoising network is trained in the following manner before deployment: providing at least one set of original images; the original images are split into training input original images and training target original images in the manner of first downsampling and then upsampling; the training input original images and the training target original images are augmented to generate a set of training input original images and a set of training target original images; and a training network model of a self-supervised deep learning network is established for the super-resolution image denoising network.

The technology disclosed relates to structured illumination microscopy (SIM). In particular, the technology disclosed relates to capturing and processing a large number of image tiles on a large image plane in real time, dividing them into sub-tiles, efficiently processing the sub-tiles, and generating an enhanced resolution image from the sub-tiles. The enhanced resolution image can be combined into an enhanced image and can be used for subsequent analysis steps.

The present disclosure relates to imaging systems and methods of use thereof for imaging with simplified structured illumination microscopy (SIM). The present methods may be simpler and less costly to build and maintain that existing SIM systems. The imaging methods may include oscillating a portion of the imaging system to scan an illumination pattern across a field of view of the sample.

The invention belongs to the field of three-dimensional topography measurement, and particularly discloses a structural illumination microscope surface topography reconstruction method and system based on virtual double differential motion, and the method comprises the steps: carrying out the SIM reconstruction and modulation and demodulation of a scanned and collected structural light image, and obtaining an axial modulation response curve of a full-view-field pixel; respectively and virtually moving the scanning position forwards and backwards for the same axial offset distance along the scanning direction to obtain corresponding virtual front and back focus modulation response curves AMR1 and AMR2; based on the sum of Gaussian functions approximated by AMR1 and AMR2, a virtual double-difference modulation response curve is constructed, and z represents a scanning position; when the virtual double-differential modulation response curve crosses a zero point, the corresponding scanning position is the surface height of the sample, and corresponds to the peak position of AMR; therefore, the surface morphology reconstruction of the sample is realized. According to the invention, rapid and high-precision morphology reconstruction of a complex surface can be realized.

A digital imaging method is disclosed that can be used for structured illumination microscopy to obtain a super-resolution image of a sample having a distribution of fluorophores that emit light when illuminated by an excitation beam, the digital imaging method using an innovative algorithm, wherein a super-resolution image is reconstructed in a cut plane of the sample on which the excitation beam is focused, while also taking into account light contributions from fluorophores located in the out-of-focus plane. The digital image processing method of the present disclosure may be implemented by a computer-run program, and may be used in a related structured illumination microscopy method (SIM).

A spatial filter for a confocal microscope comprising a spinnable disk locatable in an optical path of the confocal microscope between a coherent light source providing excitation light and a target. The spinnable disk comprises a first region providing a first optical path through the disk, the first region comprising a first optical path length for the excitation light transmitted through the disk and a second region providing a second optical path through the disk, the second region comprising a second optical path length for the excitation light transmitted through the disk, the second optical path length different from the first optical path length and thereby imparting a second phase shift to the excitation light transmitted through the second region that is different from a first phase shift imparted to the excitation light transmitted through the first region.

Microscopy methods and devices that use structured illumination are described. An example method includes providing multiple digital raw images of a sample that are recorded sequentially a microscope and obtained by illuminating the sample in different phases with periodically structured illumination light. The method includes, determining, using a structured illumination microscopy (SIM) reconstruction method, an intermediate imaging result for the sample with an increased resolution compared to the raw images, where the resolution is higher than the diffraction-limited resolving power of the microscope. In addition, the above method reduces or suppressed image artifacts.

The application discloses a kind of structure light illumination super-resolution microscopic imaging methods based on four-beam interference.The method uses four-beam coherent interference illumination design, and combines the composite frequency domain filtering strategy based on three-dimensional OTF physical constraint, under the premise that no need to carry out any hardware modification to the light path of traditional interference structure light microscope (SIM) system, first realize the lateral two times super-resolution improvement and excellent optical sectioning ability, effectively overcome the off-focus background interference problem faced in thick sample imaging in the prior art.In addition, the application introduces an illumination parameter estimation algorithm based on principal component analysis (PCA), while significantly reducing the computational complexity, further improves the accuracy and stability of parameter estimation.Experimental results show that the method can high-resolution, high-fidelity, high-robustness and high-efficiency imaging for thick living cell samples, break through the limitation that traditional two-dimensional super-resolution SIM is mainly suitable for thin living cell imaging, has wide application potential and practical value.

This invention discloses a multispectral super-resolution imaging device based on compressed sensing, belonging to the field of optical microscopy. The device comprises a light source and beam expansion system, a polarization modulation and structured illumination microscopy system, an coded spectral compression imaging system, a synchronization control system, and a data processing and reconstruction system. This invention solves the problem of the trade-off between increasing the number of spectral channels and maintaining imaging speed in existing spectral-resolution structured light super-resolution microscopy. By combining compressed sensing with single-snapshot spectral coding and dispersive imaging, structured illumination, and a two-step depth unfolding network reconstruction algorithm based on a physical model, this invention can rapidly and synchronously reconstruct super-resolution images of multiple spectral channels with only a small number of compressed images acquired, significantly improving the spectral resolution of imaging without sacrificing the imaging frame rate.

The present application relates to a kind of structure light illumination microscopic imaging method based on adaptive hilbert norm, the present application aims at solving the problem of inaccurate illumination parameter estimation value and reconstruction artifact under the condition of uneven illumination stripe in existing SIM reconstruction algorithm.The method of the present application decomposes the single frame or multiple frame SIM original image collected into structure, texture and noise part;By constructing an energy minimization model containing total variation norm and adaptive hilbert norm, the texture part representing illumination pattern is accurately extracted;The extracted illumination pattern image is iteratively reconstructed with SIM original image, and finally the super-resolution image is obtained.Compared with the traditional method, the present application can accurately extract the illumination pattern from single frame original image, effectively process the non-uniform illumination stripe with local bending, and obtain more accurate super-resolution reconstruction result.The method is suitable for a variety of reconstruction methods and application scenarios, and has good application prospect in the improvement of SIM imaging quality and imaging flux.

The invention discloses a super-resolution structure illumination obvious micro-imaging method and system and computer equipment, belongs to the field of computational imaging, and solves the problems that traditional SIM reconstruction depends on global parameters and is sensitive to low SNR, and an existing deep learning method has over-smoothness and global discrimination neglects spatial heterogeneity. Comprising the following steps: acquiring a single-frame structured light illumination input image embedded with multi-direction illumination information; performing Fourier interpolation up-sampling on the input image to match the super-resolution reconstruction scale; constructing a super-resolution reconstruction network based on the conditional generative adversarial network, and designing a multi-scale Markov discriminator to match super-resolution reconstruction tasks of organelles of different scales; training the super-resolution reconstruction network based on the double-domain loss, and obtaining a super-resolution structured illumination obvious micro-imaging model; and carrying out super-resolution structured illumination obvious micro-imaging by adopting the super-resolution structured illumination obvious micro-imaging model. The method is suitable for living cell super-resolution imaging scenes.