An optical fluctuation super-resolution imaging method based on gradient difference calculation
The optical wave super-resolution imaging method based on gradient difference calculation solves the problem of limited resolution improvement in traditional super-resolution optical scintillation imaging technology, achieving higher resolution and noise resistance, and improving image quality.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SOUTH CHINA NORMAL UNIV
- Filing Date
- 2026-04-01
- Publication Date
- 2026-06-12
AI Technical Summary
Traditional super-resolution optical scintillation imaging technology is limited by image noise and the number of imaging frames when calculating higher-order cumulative quantities, resulting in limited resolution improvement, at most only one or two times.
An optical wave super-resolution imaging method based on gradient difference calculation is adopted. By acquiring time-series images of random fluctuations in fluorescence intensity, preprocessing, gradient calculation and normalization are performed to obtain modulated time-series images. Finally, the cumulative amount is calculated to obtain super-resolution images.
The resolution is increased by 2.56 times at the same order, effectively suppressing imaging noise interference and improving the anti-interference capability and image quality of super-resolution imaging.
Smart Images
Figure CN122199349A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the technical field of optical microscopy imaging, specifically to an optical wave super-resolution imaging method based on gradient difference calculation. Background Technology
[0002] Super-resolution optical scintillation imaging (SOFI) is an important super-resolution imaging technique that has demonstrated significant value in the field of biological fluorescence microscopy since its introduction in 2009. By capturing the random scintillation characteristics of fluorescent probes, acquiring time-series fluorescence images, and calculating the nth-order cumulative quantity, it compresses the system point spread function (PSF), thereby breaking through the diffraction limit of traditional wide-field microscopy and achieving… The resolution improvement is more than double. However, traditional autocorrelation or cross-correlation SOFI algorithms based on fluorescence scintillation are limited by image noise and the number of imaging frames when calculating higher-order cumulants, and can only calculate second- or third-order cumulants, thus resulting in limited resolution improvement, with a maximum of only [missing information]. or times.
[0003] Therefore, in order to ensure the fidelity of the reconstructed image and further improve the imaging resolution, it is necessary to provide a super-resolution optical scintillation imaging method based on gradient difference calculation to solve the above-mentioned technical problems. Summary of the Invention
[0004] The technical problem to be solved by the present invention is to overcome the above-mentioned technical defects and provide an optical wave super-resolution imaging method based on gradient difference calculation, which can further improve the resolution of imaging compared with traditional super-resolution optical wave imaging technology.
[0005] To solve the above-mentioned technical problems, the technical solution provided by the present invention is: an optical wave super-resolution imaging method based on gradient difference calculation, comprising the following steps: S1: Acquire time-series fluorescence images of the sample under wide-field illumination with random fluctuations in fluorescence intensity; S2: After preprocessing the time series fluorescence image in S1, solve for the absolute value of the gradient of the two-dimensional pixels of the image and synthesize it by vector to obtain the magnitude of the absolute value of the image gradient, and then normalize it. S3: Subtract the magnitude of the absolute value of the gradient corresponding to each frame after normalization from the original wide-field illumination image sequence of the imaging system, and retain the non-negative part to obtain the modulated time-series fluorescence image. S4: Perform cumulative calculation based on the modulated time-series fluorescence image to obtain the final super-resolution image.
[0006] Preferably, in step S1, the sample to be tested is irradiated with wide-field illumination under single-wavelength excitation conditions to acquire time-series fluorescence images; The time-series fluorescence image acquisition equipment includes any one of a wide-field microscopy system, a total internal reflection fluorescence microscopy system, or a rotating confocal microscopy system.
[0007] Preferably, the preprocessing in S2 includes sequentially performing spatial domain filtering, frequency domain high-pass filtering, and noise reduction and smoothing on the time-series fluorescence image; in: The spatial domain filtering employs nonlocal mean filtering. The noise reduction and smoothing process reduces image noise while preserving the image's edge and detail information.
[0008] Preferably, solving for the gradient of two-dimensional pixels in the image in S2 includes solving for the first-order partial derivatives of the image along the horizontal and vertical directions, respectively. Vector synthesis refers to obtaining the gradient magnitude of an image by taking the absolute values of the first-order partial derivatives in the horizontal and vertical directions, and then performing operations such as squaring, summing, and taking the square root.
[0009] Preferably, the wide-field illumination image sequence of the imaging system in S3 is obtained by sample convolution with a two-dimensional Gaussian point spread function.
[0010] Preferably, step S3 further includes adjusting the modulation amplitude of the differential operation by a modulation coefficient, wherein the modulation coefficient is set to 1, and an isotropic compressed light spot is obtained after modulation.
[0011] Preferably, the cumulative amount calculation in S4 is to perform autocorrelation cumulative amount calculation or cross-correlation cumulative amount calculation on the modulated time-series fluorescence image; The order of the cross-correlation cumulative calculation is second or third order.
[0012] The advantages of this invention compared to existing technologies are as follows: Compared to traditional SOFI imaging technology, this invention utilizes gradient difference calculation, achieving a resolution significantly higher than traditional SOFI at the same order, with a resolution improvement of 2.56 times. Simultaneously, in terms of image quality, the imaging method of this invention maintains fidelity, effectively suppressing the interference of imaging noise on the reconstruction results. This alleviates the industry pain point of traditional SOFI technology, where high-order cumulative quantities are easily affected by noise, amplifying artifacts, and significantly improving the anti-interference capability of super-resolution imaging. Attached Figure Description
[0013] Figure 1 This is a flowchart of the method of the present invention; Figure 2. Comparison of resolution improvement effect between the present invention and traditional SOFI imaging.
[0014] As shown in the figure: Figure 2 (a) is a simulated image of the real structure; Figure 2(b) is a wide-field image; Figure 2 (c) is a traditional second-order SOFI image; Figure 2 (d) is a traditional third-order SOFI image; Figure 2 Image (e) is a second-order super-resolution image processed based on gradient difference calculation; Figure 2 In the middle (f), the image is a third-order super-resolution image processed based on gradient difference calculation. Detailed Implementation
[0015] The present invention will now be described in further detail with reference to the accompanying drawings.
[0016] A super-resolution imaging method for optical fluctuations based on gradient difference calculation includes the following steps: S1: Under single-wavelength excitation, a wide-field illumination sample is used to acquire a time-series fluorescence image of the sample with random fluctuations in fluorescence intensity under wide-field illumination; S2: The time-series fluorescence image in S1 is subjected to non-local mean filtering in the spatial domain, converted to the frequency domain, and then subjected to high-pass filtering to expand the frequency domain. A denoising algorithm is used to denoise and smooth the time-series image to preserve edges to the greatest extent while not losing details. The absolute values of the gradients of the two-dimensional pixels of the image are then solved and vector synthesized to obtain the amplitude of the absolute values of the image gradients, which are then normalized; S3: The amplitude of the normalized absolute values of the gradients of each frame is subtracted from the wide-field illumination image sequence of the imaging system, and the non-negative part is retained to obtain a time-series fluorescence image based on gradient difference calculation. Then, the second-order and third-order cross-correlation cumulative quantities are used to obtain the final super-resolution image.
[0017] S4: The specific image calculations involved in S1-S3 are theoretically derived as follows: The set of time-series images of random fluorescence flickering obtained by wide-field illumination is as follows: ; in, The total number of imaging frames, This represents the true spatial distribution of fluorescent molecules. For position The on / off state of the fluorescent molecule at position k in the kth frame ( ), Let be the system point spread function, defined as , This represents a two-dimensional convolution operation. The system imaging noise (such as photon noise or background noise) in the k-th frame. This is the original image of the k-th frame.
[0018] In the spatial domain, first analyze each frame of the time-series image. After performing nonlocal mean filtering and transforming to the frequency domain, a high-pass filter is applied to expand the frequency domain. A denoising algorithm is then used for noise reduction and smoothing, preserving edges to the greatest extent possible without losing details, resulting in... Calculate its gradient to obtain That is, along and Direction Seeking First-order partial derivatives and : Taking the absolute value yields the magnitude of the gradient. Used to characterize image edge information: Using the preprocessed original image With normalized gradient magnitude information Perform a differential operation to obtain the modulated result. in, Let be the modulation coefficient, and take . .
[0019] After that, Retain non-negative parts According to the traditional SOFI reconstruction algorithm, for The autocorrelation or cross-correlation cumulative values are calculated to obtain the super-resolution image.
[0020] In specific implementations of this invention, as shown in the appendix Figure 1-2 As shown: The specific steps are as follows: 1) Acquire time-series images of target samples exhibiting fluorescence fluctuations using an optical imaging system; 2) Perform nonlocal mean filtering on the time series fluorescence image in the spatial domain, transform it to the frequency domain, perform high-pass filtering, expand the frequency domain, and use a denoising algorithm to denoise and smooth the time series image, preserving the edges to the greatest extent while not losing details S1; 3) Calculate the gradient of the time series image S1 and take its absolute value, then synthesize the vectors and normalize them to obtain S2; 4) Subtract the normalized result S2 from the original denoised wide-field time series image S1 and retain the non-negative part, i.e., S = max(S1-S2,0). 5) Perform second-order and third-order cross-correlation accumulation on the non-negative image sequence S to obtain the final super-resolution image.
[0021] The relevant algorithms of this invention are written in MATLAB (R2021a) and select time-series image sequences acquired from wide-field microscopy, total internal reflection fluorescence microscopy, and rotating confocal microscopy. Running the program outputs super-resolution images based on gradient difference calculations. Figure 2 (a) is a simulated image of the real structure; Figure 2 (b) is a wide-field image; Figure 2 (c) is a traditional second-order SOFI image; Figure 2 (d) is a traditional third-order SOFI image; Figure 2 (e) is a second-order super-resolution image processed based on gradient difference calculation; Figure 2 (f) is a third-order super-resolution image processed based on gradient difference calculation.
[0022] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0023] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.
Claims
1. A super-resolution imaging method for optical waves based on gradient difference calculation, characterized in that: Includes the following steps: S1: Acquire time-series fluorescence images of the sample under wide-field illumination with random fluctuations in fluorescence intensity; S2: After preprocessing the time series fluorescence image in S1, solve for the absolute value of the gradient of the two-dimensional pixels of the image and synthesize it by vector to obtain the magnitude of the absolute value of the image gradient, and then normalize it. S3: Subtract the magnitude of the absolute value of the gradient corresponding to each frame after normalization from the original illumination image sequence of the imaging system, and retain the non-negative part to obtain the modulated time-series fluorescence image.
2. The optical wave super-resolution imaging method based on gradient difference calculation according to claim 1, characterized in that: In S1, the sample to be tested is irradiated with wide-field illumination under single-wavelength excitation conditions to acquire time-series fluorescence images. The time-series fluorescence image acquisition equipment includes any one of a wide-field microscopy system, a total internal reflection fluorescence microscopy system, or a rotating confocal microscopy system.
3. The optical wave super-resolution imaging method based on gradient difference calculation according to claim 1, characterized in that: The preprocessing in S2 includes sequentially performing spatial domain filtering, frequency domain high-pass filtering, and noise reduction and smoothing on the time-series fluorescence image; in: The spatial domain filtering employs nonlocal mean filtering. The noise reduction and smoothing process reduces image noise while preserving the image's edge and detail information.
4. The optical wave super-resolution imaging method based on gradient difference calculation according to claim 3, characterized in that: The step of solving the gradient of two-dimensional pixels in the image in S2 includes solving the first-order partial derivatives of the image along the horizontal and vertical directions, respectively. Vector synthesis refers to obtaining the gradient magnitude of an image by taking the absolute values of the first-order partial derivatives in the horizontal and vertical directions, and then performing operations such as squaring, summing, and taking the square root.
5. The optical wave super-resolution imaging method based on gradient difference calculation according to claim 1, characterized in that: The wide-field illumination image sequence of the imaging system in S3 employs a sample convolution two-dimensional Gaussian point spread function.
6. The optical wave super-resolution imaging method based on gradient difference calculation according to claim 5, characterized in that: S3 also includes adjusting the modulation amplitude of the differential operation by modulating the modulation coefficient, with the modulation coefficient set to 1, resulting in an isotropic compressed light spot after modulation.
7. The optical wave super-resolution imaging method based on gradient difference calculation according to claim 1, characterized in that: The cumulative amount calculation in S4 is to perform autocorrelation cumulative amount calculation or cross-correlation cumulative amount calculation on the modulated time-series fluorescence image; the order of the cross-correlation cumulative amount calculation is second or third order.