WO2020051764A1

WO2020051764A1 - Noise reduction method and apparatus for molecular localization super-resolution imaging, and terminal device

Info

Publication number: WO2020051764A1
Application number: PCT/CN2018/104992
Authority: WO
Inventors: 屈军乐; 陈秉灵; 潘文慧; 杨志刚
Original assignee: 深圳大学
Priority date: 2018-09-11
Filing date: 2018-09-11
Publication date: 2020-03-19

Abstract

A noise reduction method and apparatus (50) for molecular localization super-resolution imaging, and a terminal device, applicable to a molecular localization super-resolution microscope. The noise reduction method for molecular localization super-resolution imaging comprises: segmenting an image obtained by the molecular localization super-resolution microscope to obtain N unit processing images, N being an integer greater than 1 (S101); performing a Poisson correction on the unit processing images to obtain first images (S102); performing principal component analysis on the first images to obtain second images (S103); extracting image reconstruction components from the second images, reconstructing the images according to the image reconstruction components, and obtaining the reconstructed unit processing images (S104); and generating a super-resolution image according to the N reconstructed unit processing images (S105). By means of the noise reduction method, in the molecular localization super-resolution imaging method, the problem of low localization precision in the case of high-density labeled samples can be solved.

Description

Noise reduction method, device and terminal device for molecular positioning super-resolution imaging

Technical field

The invention relates to the technical field of image processing, and in particular, to a method, a device, and a terminal device for reducing noise of molecular localized super-resolution imaging.

Background technique

Fluorescence microscopy is widely used in cell microbiology imaging. Among them, molecular localized super-resolution imaging is a representative super-resolution fluorescence imaging technology. This technology combines single-molecule imaging with high-precision molecular positioning algorithms on the basis of fluorescence microscopy to achieve ultra-high spatial resolution of 20-30 nm to observe the ultra-microstructure in cells. Single-molecule localization imaging methods include light-activated localization microscopy, random optical reconstruction microscopy, and fluorescent light-sensitive localization microscopy. They all use random activation of fluorescence to turn on a single photoactive molecule and then image and bleach these single molecules. Only one fluorescent probe emits light within the diffraction limit. Then, all the single-molecule coordinates obtained by the light activation and imaging / bleaching cycles are obtained through the positioning algorithm, and a final super-resolution image is obtained by reconstruction.

However, due to the increase in molecular density, during the localization process of conventional molecular localization super-resolution imaging systems, there are many cases where molecules overlap with each other in a single frame of the image, which results in the localization algorithm locating a single molecule. In the case of high-density labeled samples, the positioning accuracy is low.

Summary of the Invention

The main purpose of the present invention is to propose a noise reduction method, device and terminal device for molecular localization super-resolution imaging, so as to solve the problem of low localization accuracy of the molecular localization super-resolution imaging method in the case of high-density labeled samples.

In order to achieve the above object, the first aspect of the present invention provides a molecular localization super-resolution imaging method for noise reduction, which is applied to a molecular localization super-resolution microscope. The molecular localization super-resolution imaging method includes:

Segmenting an image obtained by a molecular localization super-resolution microscope to obtain N unit processed images, where N is an integer greater than one;

Performing Poisson correction on the unit processed image to obtain a first image;

Performing a principal component analysis process on the first image to obtain a second image;

Extracting an image reconstruction component from the second image, reconstructing an image according to the image reconstruction component, and obtaining a reconstructed unit-processed image;

An image is processed according to the N reconstructed units to generate a super-resolution image.

Optionally, performing the Poisson correction on the unit-processed image to obtain a first image includes:

Acquiring a noise signal and an original image signal in the unit processed image;

Adjusting the distribution of the noise signal intensity and the original image signal intensity through the Poisson correction to obtain a first image;

The formula for adjusting the distribution of the intensity of the noise signal and the intensity of the original image signal is:

among them,

Is the intensity on the k-th pixel detected in the original image of the i-th frame,

Is the average intensity of the entire original image of the i-th frame, and k is a positive integer.

Optionally, performing the principal component analysis on the first image to obtain a second image includes:

According to the number of frames in the first image, A first image coordinate systems are established, and the variables in the a first image coordinate system are mapped to the principal elements of the a + 1th first image coordinate system To obtain the variables of the a + 1th first image coordinate system, wherein A is an integer greater than 1 and a is an integer greater than or equal to 1 and less than A;

According to the mapped first image coordinate system, a covariance matrix is established, and the formula is:

among them,

Are the intensity values of the i-th and j-th frames in k pixels in the first image, and

The average intensity values of the first image at the i-th and j-th frames;

Calculate the eigenvalues in the covariance matrix, the formula is:

C = UDV ^T ,

Where V is a right singular vector, V ^T is a transpose of V, including the right eigenvector of the covariance matrix, U is a left singular vector, a matrix containing the left eigenvector of the covariance matrix, and D is the The diagonal matrix corresponding to the eigenvalues of the covariance matrix;

Select all t eigenvalues in the diagonal matrix D corresponding to the eigenvalues of the covariance matrix, select the first m as the eigenvalues, and the formula is:

Among them, m indicates that the m feature values correspond to m feature images, and the m principal features determine m principal components;

Calculate the value of the P-th principal component at k pixels in the m characteristic images to obtain a second image with the formula:

V _{p, i} represents the value of the p-th feature vector in the i-th frame,

Represents the intensity value of the k-th pixel in the i-th frame after Poisson correction, and q represents the total number of frames.

Optionally, extracting an image reconstruction component from the second image, reconstructing an image based on the image reconstruction component, and obtaining a reconstructed unit-processed image includes:

The reconstruction component is extracted according to the second image, and the specific gravity of the m second images is calculated. The calculation formula is:

A projection matrix is established according to the proportions of the m second images, and the formula is:

S _m = [r ₁ S ₁ , r ₂ S ₂ , ..., r _p S _p , ..., r _m S _m ];

An image is reconstructed according to the projection matrix, and the calculation formula is:

Among them, S _m represents a projection matrix established by m according to the specific gravity of the m second images, and V _m represents a feature vector corresponding to m feature values.

Represents a q-frame image composed of k pixels after denoising;

An image is reconstructed according to the q-frame image composed of k pixels after the denoising, to obtain a reconstructed unit-processed image.

Optionally, the diagonal matrices D corresponding to the eigenvalues of the covariance matrix are sorted in descending order.

According to a second aspect of the embodiments of the present invention, a molecular positioning super-resolution imaging noise reduction device is applied to a molecular positioning super-resolution microscope. The molecular positioning super-resolution imaging noise reduction device includes:

A segmentation module for segmenting an image obtained by a molecular positioning super-resolution microscope to obtain several unit-processed images;

A correction module, configured to perform Poisson correction on the unit processed image to obtain a first image;

A principal component analysis module, configured to perform principal component analysis on the first image to obtain a second image;

A reconstruction module, configured to extract an image reconstruction component from the second image, reconstruct an image according to the image reconstruction component, and obtain a reconstructed unit-processed image;

An image generation module is configured to process an image according to a number of the reconstructed units to generate a super-resolution image.

Optionally, the correction module includes:

An obtaining unit, configured to obtain a noise signal and an original image signal in an image processed by the unit;

An adjusting unit, configured to adjust the distribution of the intensity of the noise signal and the intensity of the original image signal through the Poisson correction to obtain a first image;

among them,

Optionally, the principal component analysis module includes:

A coordinate mapping unit, configured to establish A first image coordinate systems according to the number of frames in the first image, and map variables in the a first image coordinate system to an a + 1 first The principal element of the image coordinate system, to obtain the a + 1st variables of the first image coordinate system, where A is an integer greater than 1 and a is an integer greater than or equal to 1 and less than A;

A covariance matrix establishing unit is configured to establish a covariance matrix based on the mapped first image, and a formula is:

among them,

The average intensity values of the first image at the i-th and j-th frames;

The eigenvalue calculation unit is configured to calculate an eigenvalue in the covariance matrix, and the formula is:

C = UDV ^T ,

The principal component analysis unit is configured to calculate the value of the P-th principal component at k pixels in the m characteristic images to obtain a second image, and the formula is:

v _{p, i} represents the value of the p-th feature vector in the i-th frame,

A third aspect of the embodiments of the present invention provides a terminal device including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the foregoing is implemented. Various Steps in a Noise Reduction Method for Molecular Localization Super-Resolution Imaging

A fourth aspect of the embodiments of the present invention provides a storage medium. The storage medium is a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned molecular localization super-resolution imaging is implemented. Steps in the noise reduction method.

The noise reduction method for molecular localized super-resolution imaging provided by the present invention extracts several unit processed images from the image obtained by the molecular localized super-resolution microscope, and performs Poisson correction on each unit processed image to reduce the high linear correlation in the labeled sample. Variables, and then carry out principal component analysis on the basis of the corrected image, keep as much of the main information of the original image on the time series as possible, remove excess noise, that is, reduce the repeated parts of the image reconstruction components, and improve molecular localization super-resolution imaging The positioning accuracy of the single molecule in the positioning process of the system was used to obtain a super-resolution image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic flowchart of implementing a noise reduction method for molecular localized super-resolution imaging provided in Embodiment 1 of the present invention; FIG.

FIG. 2 is a schematic flowchart of the detailed steps of step S102 in FIG. 1; FIG.

FIG. 3 is a schematic flowchart of the detailed steps of step S103 in FIG. 1; FIG.

FIG. 4 is a schematic flowchart of the detailed steps of step S104 in FIG. 1; FIG.

5 is a schematic structural diagram of a noise reduction device for molecular localized super-resolution imaging provided in Embodiment 2 of the present invention;

6 is a schematic structural diagram of a correction module in FIG. 5;

FIG. 7 is a schematic structural diagram of a principal component analysis module in FIG. 5.

The realization of the purpose, functional characteristics and advantages of the present invention will be further explained with reference to the embodiments and the drawings.

detailed description

It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

It should be noted that, in this article, the terms "including", "including" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, It also includes other elements not explicitly listed, or elements inherent to such a process, method, article, or device. Without more restrictions, an element limited by the sentence "including a ..." does not exclude that there are other identical elements in the process, method, article, or device that includes the element.

In this document, the use of suffixes such as "module," "component," or "unit" to represent elements is merely for the benefit of the description of the present invention, and it does not have a specific meaning in itself. Therefore, "module" and "component" can be used in combination.

In the following description, the serial numbers of the embodiments of the invention are only for description, and do not represent the advantages of the embodiments.

Example one

As shown in FIG. 1, an embodiment of the present invention provides a method for reducing noise of molecular localized super-resolution imaging, which is applied to molecular localized super-resolution microscopy such as a random optical reconstruction microscope to improve the imaging process of cells and microorganisms; Denoising methods for resolution imaging include:

S101. Segment an image obtained by a molecular localization super-resolution microscope to obtain N unit processed images, where N is an integer greater than one.

In the above step S101, the image obtained by the molecular localization super-resolution microscope is an image obtained by fluorescence information emitted from the sample collected after the fluorescent probe has finished flickering within a certain time. As the molecular density of the collected sample group gradually increases, in the image obtained at this time, there are many cases where molecules overlap with each other in a single frame image, which will cause the positioning algorithm to locate the single molecule incorrectly.

In the embodiment of the present invention, segmenting an image obtained by a molecular positioning super-resolution microscope to obtain a unit-processed image can improve positioning accuracy and a noise reduction effect of molecular positioning super-resolution imaging. In specific applications, the specific segmentation method of the image can be arbitrary, reducing the image processing area; for example: segmenting according to the number of frames of the image, segmenting according to the pixels of a single frame image, or dividing the image according to the number of frames, and then The pixels are divided to obtain a unit-processed image, which is not specifically limited in the implementation of the present invention.

In specific applications, when processing unit-processed images, multiple unit-processed images can be processed simultaneously without reducing image processing efficiency.

S102. Perform Poisson correction on the unit processed image to obtain a first image.

In the above step S102, performing Poisson correction on the unit-processed image manifests itself as: correcting the dependence of the detection noise on the signal intensity similar to the Poisson distribution, and retaining the linear relationship between the variables in the unit-processed image, such as different pixels in the image Linear relationship between the intensity vectors.

As shown in FIG. 2, the embodiment of the present invention further details the foregoing step S102. In step S102, performing Poisson correction on the unit-processed image, and obtaining the first image may include:

S1021. Acquire a noise signal and an original image signal in the unit processed image.

In the above step S1021, the source of the noise signal is the molecular positioning super-resolution imaging system. During the imaging process, photon noise and other sensor-based noise sources contribute to different proportions at different signal levels, resulting in the distribution of the noise signal. It is related to the intensity of the image signal; the signal intensity of the original image signal is expressed as the brightness at each pixel in the image; in the embodiment of the present invention, obtaining the original image signal is to obtain the brightness at each pixel in the image and the distribution of the noise signal Related to the brightness at each pixel in the image.

S1022. Adjust the distribution of the noise signal intensity and the original image signal intensity through the Poisson correction to obtain a first image.

Where is the intensity at the k-th pixel detected in the original image of the i-th frame, is the average intensity of the entire original image in the i-th frame, and k is a positive integer.

In the above step S1022, the formula for adjusting the distribution of the noise signal intensity and the original image signal intensity indicates that the obtained original image intensity is divided by the square root of the average intensity at each time point; the first image is a Poisson correction process on the original image After the image.

In the embodiment of the present invention, Poisson correction is used to adjust the distribution of the intensity of the noise signal and the intensity of the original image signal to reduce the correlation between the distribution of the noise signal and the distribution of the original image signal, while retaining the unit processing variables in the image A linear relationship, such as a linear relationship between the intensity vectors of different pixels in an image.

S103. Perform principal component analysis processing on the first image to obtain a second image.

In the above step S103, a principal component analysis process is performed on the original image after the Poisson correction process is performed. The principal component analysis is a spatial mapping method, which can delete repeated variables or closely related variables to establish as few as possible The new variables make the new variables irrelevant to each other, and the new variables retain the original information as much as possible in terms of reflecting the topic's information.

As shown in FIG. 3, the embodiment of the present invention further details the foregoing step S103. Performing the principal component analysis on the first image in the foregoing step S103 includes:

S1031. According to the number of frames in the first image, establish A first image coordinate systems, and map the variables in the a first image coordinate system to the a + 1th first image coordinate system. The principal element obtains the variables of the a + 1th first image coordinate system, where A is an integer greater than 1 and a is an integer greater than or equal to 1 and less than A.

In the above step S1031, according to the number of frames in the first image, A first image coordinate systems are established, and A is still smaller than the number of frames in the first image; because images with different frame numbers in the first image have different , By mapping the variables in the coordinate system to the principal elements in the adjacent coordinate system, the linear correlation between the variables can be reduced.

S1032. A covariance matrix is established according to the mapped first image coordinate system, and the formula is:

among them,

The average intensity values of the first image at the i-th and j-th frames, respectively.

S1033. Calculate a second eigenvalue in the second covariance matrix, the formula is:

C = UDV ^T ,

Where V is a right singular vector, V ^T is a transpose of V, including a right eigenvector of the second covariance matrix, U is a left singular vector, a matrix including a left eigenvector of the second covariance matrix, D is a diagonal matrix corresponding to the eigenvalues of the second covariance matrix.

In the above steps S1032 and S1033, the linear correlation between variables is measured through the covariance matrix; at the same time, for the purpose of principal component analysis, the obtained covariance matrix is subjected to singular value decomposition to extract the eigenvectors of C and Eigenvalues to analyze the matrix and write it as the product of three matrices.

In specific applications, when the sample is n-dimensional data, its covariance is actually a covariance matrix (symmetric square matrix), and the side length of the square matrix is Cn ² . For example: for 3D data (x, y, z), the covariance is:

cov (x, x) cov (x, y) cov (x, z)

C = cov (y, x) cov (y, y) cov (y, z).

cov (z, x) cov (z, x) cov (z, z)

In order to obtain the covariance matrix, the original variable matrix needs to be transformed so that all elements of the covariance matrix except for the diagonals become 0; the above-mentioned matrix change is a matrix diagonalization process, which is completed by singular value decomposition. Among them, singular value decomposition is a decomposition method that can be applied to any matrix. A matrix is decomposed into the form of C = UΣVT, U is a left singular vector matrix, and Σ is a diagonal matrix composed of singular values. The values are arranged from large to small, and V is a right singular vector. The sigma matrix obtained by the decomposition is a diagonal matrix, and the eigenvalues are arranged from large to small. The special eigenvectors corresponding to these eigenvalues describe the change direction of the matrix, that is, from the major change to the minor change. arrangement. When the matrix is high-dimensional, then this matrix is a linear transformation in a high-dimensional space. This linear change may not be represented by pictures, but it is conceivable that this transformation also has many transformation directions. The first N feature vectors obtained through eigenvalue decomposition correspond to the N major change directions of this matrix. Using these first N directions of change, the most important features of this matrix can be extracted. According to the above features, after reducing the correlation between variables, the variables can reflect the subject's information as much as possible while retaining the original information.

In specific applications, the magnitude of the variance describes the amount of information of a variable. Generally, the direction of the large variance is the direction of the signal, and the direction of the small variance is the direction of the noise. In digital signal processing, it is often necessary to improve the signal and noise. Ratio, which is the signal-to-noise ratio. The singular value decomposition can find the axis with the largest variance, that is, the first singular vector, and the axis with the second largest variance is the second singular vector. In general, a set of mutually orthogonal coordinate axes is sequentially found in the original space. The first axis makes the variance the largest, and the second axis makes the plane orthogonal to the first axis such that The variance is the largest, and the third axis is the largest in the plane orthogonal to the first and second axes. Suppose that in N-dimensional space, N such coordinate axes can be found, and the first r are taken to approximate this space, then compression from an N-dimensional space to an r-dimensional space is achieved, but the r coordinate axes selected at this time It can make space compression minimize the loss of data.

In one embodiment, the diagonal matrices D corresponding to the eigenvalues of the second covariance matrix are sorted in descending order.

S1034. Select all t eigenvalues in the diagonal matrix D corresponding to the eigenvalues of the covariance matrix. Select the first m eigenvalues as the eigenvalues. The formula is:

V _{p, i} represents the value of the p-th feature vector in the i-th frame,

In the above step S1034, since one feature vector corresponds to one projection image, m projection images (S1, S2, ..., Sm) are obtained, that is, the second image.

S104. Extract an image reconstruction component from the second image, reconstruct an image according to the image reconstruction component, and obtain a reconstructed unit-processed image.

In the above step S104, an image reconstruction component is extracted from the original image after Poisson correction and dimensionality reduction. The extracted reconstruction component avoids repeated extraction on the one hand, and it is necessary to ensure reconstruction based on this reconstruction component. No information is lost in the image.

As shown in FIG. 4, the embodiment of the present invention further describes the foregoing step S104. In step S104, an image reconstruction component is extracted from the second image, and an image is reconstructed according to the image reconstruction component to obtain a reconstruction. The post-processing image includes:

S1041. Extract reconstruction components according to the second image, and calculate the proportions of the m second images. The calculation formula is:

S1042. A projection matrix is established according to the proportions of the m second images, and the formula is:

S _m = [r ₁ S ₁ , r ₂ S ₂ , ..., r _p S _p , ..., r _m S _m ];

S1043. The image is reconstructed according to the projection matrix, and the calculation formula is:

Represents a q-frame image composed of k pixels after denoising.

In the above step S1043, the q-frame image is q images composed of k pixels,

Where i represents the i-th frame image composed of k pixels, and q is the total number of frames in the image. For example, if the number of image frames q is 4, and the pixel k is 1000, then the q frame image is the first frame image composed of 1000 pixels, the second frame image composed of 1000 pixels, and the third frame composed of 1000 pixels. A frame image and a fourth frame image composed of 1000 pixels.

In the above steps S1041 to S1043, a feature image is calculated by using a feature vector. If m feature values are selected, there are m feature images (S1, S2, ..., Sm), and the second image is a projection image After extracting the reconstructed components from the second image, because the proportion of each feature value is different, you need to calculate the proportion r _{p of} each feature value in its feature image, that is, m feature values in m Specific gravity in the second image; when performing image reconstruction, a projection matrix needs to be established according to the specific gravity of the m second images, and the image is reconstructed based on the projection matrix to form a reconstructed unit processed image. The processing is complete.

S105. Process the image according to the N reconstructed units to generate a super-resolution image.

In the above step S105, all the reconstructed unit processed images are combined to generate a complete super-resolution image. At this time, there is less noise in the image, and the positioning accuracy of the single molecule is high.

The method for noise reduction of molecular localized super-resolution imaging provided by the embodiment of the present invention extracts several unit processed images from the images obtained by the molecular localized super-resolution microscope, performs Poisson correction on each unit processed image, and reduces the linear correlation in the labeled sample. High variable, and then perform principal component analysis based on the corrected image, retaining as much of the main information of the original image on the time series as possible, sharpening the point spread function that repeatedly appears within a diffraction limit, and removing excess noise, ie Reduce the repetitive part of the image reconstruction components, improve the single molecule positioning accuracy during the localization process of the molecular localization super-resolution imaging system, and obtain super-resolution images.

Example two

As shown in FIG. 5, an embodiment of the present invention provides a molecular positioning super-resolution imaging noise reduction device 50, which is applied to a molecular positioning super-resolution microscope. The molecular positioning super-resolution imaging noise reduction device 50 includes:

A segmentation module 51, configured to segment an image obtained by a molecular positioning super-resolution microscope to obtain several unit-processed images;

A correction module 52, configured to perform Poisson correction on a unit processed image to obtain a first image;

A principal component analysis module 53 configured to perform principal component analysis on a first image to obtain a second image;

A reconstruction module 54 configured to extract an image reconstruction component from the second image, reconstruct an image according to the image reconstruction component, and obtain a reconstructed unit-processed image;

An image generation module 55 is configured to process an image according to a number of reconstructed units to generate a super-resolution image.

As shown in FIG. 6, in the embodiment of the present invention, the correction module 52 includes:

An obtaining unit 521, configured to obtain a noise signal and an original image signal in an image processed by the unit;

An adjusting unit 522, configured to adjust the distribution of the intensity of the noise signal and the intensity of the original image signal through the Poisson correction to obtain a first image;

among them,

As shown in FIG. 7, the principal component analysis module 53 includes:

A coordinate mapping unit 531, configured to establish A first image coordinate systems according to the number of frames in the first image, and map variables in the a first image coordinate system to an a + 1th A principal element of an image coordinate system to obtain the variables of the a + 1th first image coordinate system, wherein A is an integer greater than 1 and a is an integer greater than or equal to 1 and less than A;

A covariance matrix establishing unit 532 is configured to establish a covariance matrix according to the mapped first image, and a formula is:

among them,

The average intensity values of the first image at the i-th and j-th frames;

The eigenvalue calculation unit 533 is configured to calculate an eigenvalue in the covariance matrix, and the formula is:

C = UDV ^T ,

The principal component determining unit 534 is configured to select all t eigenvalues in the diagonal matrix D corresponding to the eigenvalues of the covariance matrix, and select the first m as the eigenvalues, the formula is:

The principal component analysis unit 535 is configured to calculate the value of the P-th principal component in k pixels among the m characteristic images to obtain a second image, and the formula is:

V _{p, i} represents the value of the p-th feature vector in the i-th frame,

In one embodiment, the reconstruction module 54 includes a specific gravity calculation unit, a projection matrix establishment unit, and an image reconstruction unit, where:

The specific gravity calculation unit is configured to extract a reconstruction component according to the second image, and calculate the specific gravity of the m second images, and the calculation formula is:

A projection matrix establishing unit is configured to establish a projection matrix according to the proportions of the m second images, and a formula is:

S _m = [r ₁ S ₁ , r ₂ S ₂ , ..., r _p S _p , ..., r _m S _m ],

An image reconstruction unit is configured to reconstruct an image according to the projection matrix, and a calculation formula is:

Represents a q-frame image composed of k pixels after denoising;

In specific applications, q-frame images are q images composed of k pixels.

Where i represents the i-th frame image composed of k pixels, and q is the total number of frames in the image. For example, if the number of image frames q is 4, and the pixel k is 1000, the q frame image is the first frame image composed of 1000 pixels, the second frame image composed of 1000 pixels, and the third frame composed of 1000 pixels A frame image and a fourth frame image composed of 1000 pixels.

An embodiment of the present invention further provides a terminal device including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, implementation is as described in the first embodiment. Steps in the noise reduction method of molecular localization super-resolution imaging

An embodiment of the present invention also provides a storage medium. The storage medium is a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the molecular positioning as described in the first embodiment is implemented. Various steps in the noise reduction method of super-resolution imaging.

The above are only preferred embodiments of the present invention, and thus do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the description and drawings of the present invention, or directly or indirectly used in other related technical fields All are included in the patent protection scope of the present invention.

Claims

A noise reduction method for molecular localization super-resolution imaging, which is characterized in that it is applied to a molecular localization super-resolution microscope. The molecular localization super-resolution imaging includes:

Segmenting an image obtained by a molecular localization super-resolution microscope to obtain N unit processed images, where N is an integer greater than one;

Performing Poisson correction on the unit processed image to obtain a first image;

Performing a principal component analysis process on the first image to obtain a second image;

Extracting an image reconstruction component from the second image, reconstructing an image according to the image reconstruction component, and obtaining a reconstructed unit-processed image;

An image is processed according to the N reconstructed units to generate a super-resolution image.
The method for noise reduction of molecular localized super-resolution imaging according to claim 1, wherein the performing a Poisson correction on the unit processed image to obtain a first image comprises:

Acquiring a noise signal and an original image signal in the unit processed image;

Adjusting the distribution of the noise signal intensity and the original image signal intensity through the Poisson correction to obtain a first image;

The formula for adjusting the distribution of the intensity of the noise signal and the intensity of the original image signal is:

among them,
Is the intensity on the k-th pixel detected in the original image of the i-th frame,
Is the average intensity of the entire original image of the i-th frame, and k is a positive integer.
The method of claim 1, wherein performing principal component analysis on the first image to obtain a second image comprises:

According to the number of frames in the first image, A first image coordinate systems are established, and the variables in the a first image coordinate system are mapped to the principal elements of the a + 1th first image coordinate system To obtain the variables of the a + 1th first image coordinate system, wherein A is an integer greater than 1 and a is an integer greater than or equal to 1 and less than A;

According to the mapped first image coordinate system, a covariance matrix is established, and the formula is:

among them,
Are the intensity values of the i-th and j-th frames in k pixels in the first image, and
The average intensity values of the first image at the i-th and j-th frames;

Calculate the eigenvalues in the covariance matrix, the formula is:

C = UDV T ,

Where V is a right singular vector, V T is a transpose of V, including the right eigenvector of the covariance matrix, U is a left singular vector, a matrix containing the left eigenvector of the covariance matrix, and D is the The diagonal matrix corresponding to the eigenvalues of the covariance matrix;

Select all t eigenvalues in the diagonal matrix D corresponding to the eigenvalues of the covariance matrix, select the first m as the eigenvalues, and the formula is:

Among them, m indicates that the m feature values correspond to m feature images, and the m principal features determine m principal components;

Calculate the value of the P-th principal component at k pixels in the m characteristic images to obtain a second image with the formula:

V p, i represents the value of the p-th feature vector in the i-th frame,
Represents the intensity value of the k-th pixel in the i-th frame after Poisson correction, and q represents the total number of frames.
The method for noise reduction of molecular localized super-resolution imaging according to any one of claims 1 to 3, wherein the image reconstruction component is extracted from the second image, and reconstructed according to the image reconstruction component Image, obtaining the reconstructed unit processed image includes:

The reconstruction component is extracted according to the second image, and the specific gravity of the m second images is calculated. The calculation formula is:

A projection matrix is established according to the proportions of the m second images, and the formula is:

S m = [r 1 S 1 , r 2 S 2 , ..., r p S p , ..., r m S m ];

An image is reconstructed according to the projection matrix, and the calculation formula is:

Among them, S m represents a projection matrix established by m according to the specific gravity of the m second images, and V m represents a feature vector corresponding to m feature values.
Represents a q-frame image composed of k pixels after denoising;

An image is reconstructed according to the q-frame image composed of k pixels after the denoising, to obtain a reconstructed unit-processed image.
The method of claim 4, wherein the diagonal matrix D corresponding to the eigenvalues of the covariance matrix is sorted in descending order.
A noise reduction device for molecular positioning super-resolution imaging, which is characterized in that it is applied to a molecular positioning super-resolution microscope. The noise reduction device for molecular positioning super-resolution imaging includes:

A segmentation module for segmenting an image obtained by a molecular positioning super-resolution microscope to obtain several unit-processed images;

A correction module, configured to perform Poisson correction on the unit processed image to obtain a first image;

A principal component analysis module, configured to perform principal component analysis on the first image to obtain a second image;

A reconstruction module, configured to extract an image reconstruction component from the second image, reconstruct an image according to the image reconstruction component, and obtain a reconstructed unit-processed image;

An image generation module is configured to process an image according to a number of the reconstructed units to generate a super-resolution image.
The noise reduction device for molecular localized super-resolution imaging according to claim 6, wherein the correction module comprises:

An obtaining unit, configured to obtain a noise signal and an original image signal in an image processed by the unit;

An adjusting unit, configured to adjust the distribution of the intensity of the noise signal and the intensity of the original image signal through the Poisson correction to obtain a first image;

The formula for adjusting the distribution of the intensity of the noise signal and the intensity of the original image signal is:

among them,
Is the intensity on the k-th pixel detected in the original image of the i-th frame,
Is the average intensity of the entire original image of the i-th frame, and k is a positive integer.
The noise reduction device for molecular localized super-resolution imaging according to claim 6, wherein the principal component analysis module comprises:

A coordinate mapping unit, configured to establish A first image coordinate systems according to the number of frames in the first image, and map variables in the a first image coordinate system to an a + 1 first The principal element of the image coordinate system, to obtain the a + 1st variables of the first image coordinate system, where A is an integer greater than 1 and a is an integer greater than or equal to 1 and less than A;

A covariance matrix establishing unit is configured to establish a covariance matrix according to the mapped first image, and a formula is:

among them,
Are the intensity values of the i-th and j-th frames in k pixels in the first image, and
The average intensity values of the first image at the i-th and j-th frames;

The eigenvalue calculation unit is configured to calculate an eigenvalue in the covariance matrix, and the formula is:

C = UDV T ,

Where V is a right singular vector, V T is a transpose of V, including the right eigenvector of the covariance matrix, U is a left singular vector, a matrix containing the left eigenvector of the covariance matrix, and D is the The diagonal matrix corresponding to the eigenvalues of the covariance matrix;

Select all t eigenvalues in the diagonal matrix D corresponding to the eigenvalues of the covariance matrix, select the first m as the eigenvalues, and the formula is:

Among them, m indicates that the m feature values correspond to m feature images, and the m principal features determine m principal components;

The principal component analysis unit is configured to calculate the value of the P-th principal component at k pixels in the m characteristic images to obtain a second image, and the formula is:

v p, i represents the value of the p-th feature vector in the i-th frame,
Represents the intensity value of the k-th pixel in the i-th frame after Poisson correction, and q represents the total number of frames.
A terminal device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, and is characterized in that when the processor executes the computer program, it implements claim 1 Each step in the noise reduction method for molecular localized super-resolution imaging according to any one of 5 to 5.
A storage medium is a computer-readable storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, the computer program according to any one of claims 1 to 5 is implemented. Various steps in the noise reduction method of molecular localization super-resolution imaging.