Multi-spatial filtering optical fluctuation imaging exciting light correction method

Abstract

The invention discloses a multi-spatial filtering optical fluctuation imaging exciting light correction method, belongs to the technical field of super-resolution microscopic imaging and computational imaging, and aims to solve the problems that in the reconstruction process of an existing signal fluctuation imaging technology, a nonlinear effect can be generated on the original intensity difference of an image, so that the light intensity difference in the image is greatly increased, and the image quality is influenced. And the image reconstruction performance is reduced. According to the method, firstly, wavelet decomposition is carried out on an original signal time sequence by using wavelet decomposition, and then an original normalization process is replaced by an operation of filtering a wavelet component with the lowest frequency, so that the intensity difference of an original image is reduced, the nonlinear effect of a reconstruction process is reduced, and the purpose of light intensity correction is finally achieved.

Description

Technical Field

[0001] This invention belongs to the fields of super-resolution imaging and computational imaging, and relates to a multi-spatial filtering method for optical wave imaging excitation light correction. By using wavelet decomposition to decompose the original signal time series, the lowest-frequency wavelet components are filtered out, thereby reducing the intensity differences in the original image and the nonlinear effects of the reconstruction process, ultimately achieving the purpose of light intensity correction. Background Art

[0002] Fluorescence super-resolution methods based on molecular intensity signal fluctuation models utilize only the physical model of random fluctuations in fluorescence molecular intensity, without relying on any hardware modulation. This makes them a flexible and highly cost-effective super-resolution approach. Due to their independence from hardware system limitations, they can be flexibly coupled to a variety of imaging modalities. Furthermore, the wavelet transform is a new transform analysis method that inherits and develops the localization principle of the short-time Fourier transform while overcoming the drawback of frequency-invariant window size. It can provide a frequency-dependent wavelet window, making it an ideal tool for time-frequency signal analysis and processing. Summary of the Invention

[0003] A brief overview of the present invention is provided below to provide a basic understanding of certain aspects of the present invention. It should be understood that this overview is not an exhaustive overview of the present invention. It is not intended to identify key or important aspects of the present invention, nor is it intended to limit the scope of the present invention. Its purpose is simply to present certain concepts in a simplified form as a prelude to the more detailed description discussed later.

[0004] In view of this, the present invention provides a multi-spatial filtering optical wave imaging excitation light correction method, which uses wavelet decomposition to decompose the original signal time series and filter out the lowest-frequency wavelet component, thereby reducing the intensity difference of the original image and reducing the nonlinear effect of the reconstruction process, thereby ultimately achieving the purpose of light intensity correction.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] Solution 1: The present invention provides a multi-spatial filtering optical wave imaging excitation light correction method. The microscopic imaging process includes the following steps:

[0007] Step a: repeatedly collect the light intensity distribution of the sample to be tested using the imaging system to obtain a set of time-series images of the sample with a size of M×N×L.

[0008] Step b: For the time-series image sequence, perform single-pixel decomposition on the sequence along the first and second dimensions, and decompose the image sequence into single-pixel intensity time-series sequences of length L, totaling M×N.

[0009] Step c: performing one-dimensional n-th-order wavelet decomposition on each decomposed time series to obtain multiple groups of wavelet components from 0 to n.

[0010] Step d: Perform wavelet filtering on the time series, and set the wavelet component with order 0 to 0.

[0011] Step e: reorganize the remaining wavelet components in the wavelet domain to obtain a filtered time series.

[0012] Step f: perform signal fluctuation analysis and reconstruction on the filtered time series to obtain reconstructed intensity results, totaling M×N.

[0013] Step g: Combine and reconstruct the M×N intensity results to obtain a signal fluctuation reconstructed image corrected for light intensity.

[0014] Preferably, in step a, the sample is stained with a dye having intermittent fluorescence. These fluorescent molecular groups have independent molecular brightness that varies with time. The fluorescence signal at position r and time t can be expressed as:

[0015] F(r,t)=h(rr k )·c k ·ω k (t)

[0016] Here, h, c, and ω represent the point spread function of the corresponding microscope, the molecular brightness constant, and the function of the molecular brightness fluctuation over time, respectively.

[0017] Preferably, the imaging process described in step a requires more than 1000 independent imagings.

[0018] Preferably, the obtained time series needs to be decomposed by one-dimensional wavelet, and the decomposition process can be expressed as:

[0019]

[0020] Preferably, during the wavelet decomposition, a one-dimensional Daubechies-6 wavelet filter is used to decompose the signal into 7 levels.

[0021] Preferably, the reconstruction process described in step f can be performed using quadratic time cumulants, and the reconstruction process can be expressed as:

[0022] G2(r)=<δF(r,t)·δF(r,t)> t

[0023] δF(r,t)=F(r,t)-<F(r,t)> t

[0024] Among them, t is the time-averaged function.

[0025] Preferably, in the reconstruction process described in step f, the expression of the second-order time cumulant is expanded. When the preset conditions are met, the cross-correlation terms of the expanded expression of the second-order time cumulant are regarded as zero, so that the second-order time cumulant is expressed as the sum of squares of the corresponding brightness constant weighted point spread functions. The expression of the second-order time cumulant G2 is expanded to obtain the following formula:

[0026]

[0027] Preferably, in the reconstruction process described in step f, it is assumed that the luminous intensity of each fluorescent molecule is an independent individual fluctuation. When i≠k (preset condition), the cross-correlation term in the expansion is considered to be zero, and the second-order time cumulant G2 is expressed as the sum of the squares of the weighted point spread function corresponding to the brightness constant γ, as follows:

[0028]

[0029] Preferably, the reconstruction process described in step f can be performed using quadratic time cumulants, and the reconstruction process can be expressed as:

[0030]

[0031] Where U is the point spread function of the system.

[0032] Preferably, the reconstruction process described in step f can be performed using second-order variance, and the reconstruction process can be expressed as:

[0033]

[0034] Preferably, the reconstruction process described in step f can be performed using second-order covariance, and the reconstruction process can be expressed as:

[0035]

[0036] Beneficial effects:

[0037] This invention is based on the signal fluctuation imaging technology, which boasts high resolution, ease of use, and the absence of additional hardware. Furthermore, it applies a nonlinear effect to the original intensity differences in the image, significantly increasing the intensity differences within the image and reducing the performance of image reconstruction. This innovative method uses wavelet decomposition to decompose the original signal time series. The subsequent normalization process is replaced by filtering out the lowest-frequency wavelet components, thereby reducing the intensity differences in the original image and the nonlinear effects of the reconstruction process, ultimately achieving the goal of intensity correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flowchart of the multi-spatial filtering optical wave imaging excitation light correction method of the present invention. DETAILED DESCRIPTION

[0039] Exemplary embodiments of the present invention are described below with reference to the accompanying drawings. For the sake of clarity and conciseness, not all features of an actual implementation are described in this specification. However, it should be understood that in the process of developing any such actual implementation, many implementation-specific decisions must be made in order to achieve the developer's specific goals, such as meeting those constraints related to the system and business, and these constraints may vary from implementation to implementation. In addition, it should be understood that although the development work may be very complex and time-consuming, it is a routine task for those skilled in the art who benefit from the disclosure of the present invention.

[0040] It is also necessary to explain here that, in order to avoid obscuring the present invention due to unnecessary details, the accompanying drawings only show the device structure and / or processing steps closely related to the solution according to the present invention, while other details that are not closely related to the present invention are omitted.

[0041] Example 1: The present invention provides a multi-spatial filtering optical wave imaging excitation light correction method.

[0042] The microscopy imaging process consists of the following steps:

[0043] Step a: repeatedly collect the light intensity distribution of the sample to be tested using the imaging system to obtain a set of time-series images of the sample with a size of M×N×L.

[0044] Step b: For the time-series image sequence, perform single-pixel decomposition on the sequence along the first and second dimensions, and decompose the image sequence into single-pixel intensity time-series sequences of length L, totaling M×N.

[0045] Step c: performing one-dimensional n-th-order wavelet decomposition on each decomposed time series to obtain multiple groups of wavelet components from 0 to n.

[0046] Step d: Perform wavelet filtering on the time series, and set the wavelet component with order 0 to 0.

[0047] Step e: reorganize the remaining wavelet components in the wavelet domain to obtain a filtered time series.

[0048] Step f: perform signal fluctuation analysis and reconstruction on the filtered time series to obtain reconstructed intensity results, totaling M×N.

[0049] Step g: Combine and reconstruct the M×N intensity results to obtain a signal fluctuation reconstructed image corrected for light intensity.

[0050] More specifically, in step a, the sample is stained with a dye having intermittent fluorescence. These fluorescent molecular groups have independent molecular brightness that varies with time. The fluorescence signal at position r and time t can be expressed as:

[0051] F(r,t)=h(rr k )·c k ·ω k (t)

[0052] Here, h, c, and ω represent the point spread function of the corresponding microscope, the molecular brightness constant, and the function of the molecular brightness fluctuation over time, respectively.

[0053] More specifically, the imaging process described in step a requires more than 1000 independent imagings.

[0054] More specifically, it is necessary to perform one-dimensional wavelet decomposition on the obtained time series, and the decomposition process can be expressed as:

[0055]

[0056] More specifically, during the wavelet decomposition, a one-dimensional Daubechies-6 wavelet filter is used to multi-level decompose the signal into 7 levels.

[0057] More specifically, the reconstruction process described in step f can be performed using the quadratic time cumulant, and the reconstruction process can be expressed as:

[0058] G2(r)=<δF(r,t)·δF(r,t)> t

[0059] δF(r,t)=F(r,t)-<F(r,t)> t

[0060] Among them, t is the time-averaged function.

[0061] More specifically, in the reconstruction process described in step f, the expression of the second-order time cumulant is expanded. When the preset conditions are met, the cross-correlation terms of the expanded expression of the second-order time cumulant are regarded as zero, so that the second-order time cumulant is expressed as the sum of squares of the corresponding brightness constant weighted point spread functions. The expression of the second-order time cumulant G2 is expanded to obtain the following formula:

[0062]

[0063] More specifically, in the reconstruction process described in step f, it is assumed that the luminescence intensity of each fluorescent molecule is an independent individual fluctuation. When i ≠ k (preset condition), the cross-correlation term in the expansion is considered to be zero, and the second-order time cumulant G2 is expressed as the sum of the squares of the point spread function weighted by the corresponding brightness constant γ, as follows:

[0064]

[0065] More specifically, the reconstruction process described in step f can be performed using the quadratic time cumulant, and the reconstruction process can be expressed as:

[0066]

[0067] Where U is the point spread function of the system.

[0068] More specifically, the reconstruction process described in step f can be reconstructed using the second-order variance, and the reconstruction process can be expressed as:

[0069]

[0070] More specifically, the reconstruction process described in step f can be performed using the second-order covariance, and the reconstruction process can be expressed as:

[0071]

Claims

1. A multi-spatial filtering optical wave imaging excitation light correction method, characterized in that: The microscopy imaging process consists of the following steps: Step a: repeatedly collect the light intensity distribution of the sample to be tested using the imaging system to obtain a set of time-series images of the sample with a size of M×N×L. Step b: For the time-series image sequence, perform single-pixel decomposition on the sequence along the first and second dimensions, and decompose the image sequence into single-pixel intensity time-series sequences of length L, totaling M×N. Step c: performing one-dimensional n-th-order wavelet decomposition on each decomposed time series to obtain multiple groups of wavelet components from 0 to n. Step d: Perform wavelet filtering on the time series, and set the wavelet component with order 0 to 0. Step e: reorganize the remaining wavelet components in the wavelet domain to obtain a filtered time series. Step f: perform signal fluctuation analysis and reconstruction on the filtered time series to obtain reconstructed intensity results, totaling M×N. Step g: Combine and reconstruct the M×N intensity results to obtain a signal fluctuation reconstructed image corrected for light intensity.

2. The method for correcting excitation light for optical wave imaging using multiple spatial filters according to claim 1, wherein: In step a, the sample is stained with a dye that has intermittent fluorescence. These fluorescent molecular groups have independent molecular brightness that varies with time. The fluorescence signal at position r and time t can be expressed as: F(r,t)=h(r-r k )·c k ·ω k (t) Here, h, c, and ω represent the point spread function of the corresponding microscope, the molecular brightness constant, and the function of the molecular brightness fluctuation over time, respectively.

3. The method for correcting excitation light for optical wave imaging using multiple spatial filters according to claim 1, wherein: The imaging process described in step a requires more than 1000 independent imagings.

4. The method for correcting excitation light for optical wave imaging using multiple spatial filters according to claim 1, wherein: It is necessary to perform one-dimensional wavelet decomposition on the obtained time series, and the decomposition process can be expressed as:

5. The method for correcting excitation light for optical wave imaging using multiple spatial filters according to claim 1, wherein: During wavelet decomposition, a one-dimensional Daubechies-6 wavelet filter is used to decompose the signal into 7 levels.

6. The method for correcting excitation light for optical wave imaging using multiple spatial filters according to claim 1, wherein: The reconstruction process described in step f can be reconstructed using the quadratic time cumulant, and the reconstruction process can be expressed as: G2(r)=<δF(r,t)·δF(r,t)> t δF(r,t)=F(r,t)-<F(r,t)> t Among them, t is the time-averaged function.

7. The method for correcting excitation light for optical wave imaging using multiple spatial filters according to claim 1, wherein: In the reconstruction process described in step f, the expression of the second-order time cumulant is expanded. When the preset conditions are met, the cross-correlation terms of the expanded expression of the second-order time cumulant are regarded as zero, so that the second-order time cumulant is expressed as the sum of squares of the corresponding brightness constant weighted point spread function. The expression of the second-order time cumulant G2 is expanded to obtain the following formula:

8. The method for correcting excitation light for optical wave imaging using multiple spatial filters according to claim 1, wherein: In the reconstruction process described in step f, it is assumed that the luminescence intensity of each fluorescent molecule is an independent individual fluctuation. When i ≠ k (preset condition), the cross-correlation term in the expansion is considered to be zero, and the second-order time cumulant G2 is expressed as the sum of the squares of the point spread function weighted by the corresponding brightness constant γ, as follows:

9. The method for correcting excitation light for optical wave imaging using multiple spatial filters according to claim 1, wherein: The reconstruction process described in step f can be reconstructed using the quadratic time cumulant, and the reconstruction process can be expressed as: Where U is the point spread function of the system.

10. The method for correcting excitation light for optical wave imaging using multiple spatial filters according to claim 1, wherein: The reconstruction process described in step f can be reconstructed using the second-order variance, and the reconstruction process can be expressed as:

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