Digital image processing method and device for improving STED imaging quality and related assembly

Abstract

The embodiment of the invention provides a digital image processing method and device for improving STED imaging quality and a related assembly, and the processing method comprises the steps: carrying out the imaging of a fluorescent bead through an STED microscope, carrying out the first image processing, Fourier transform processing and interception processing of an imaged image, obtaining an FFT image, and carrying out the first image processing, Fourier transform processing and interception processing of the FFT image. And then threshold processing, inverse Fourier transform and secondary image processing are carried out on the FFT image with the noise to obtain a final STED image, the background noise of the image is reduced, and the image resolution is improved. The processing method is simple, does not need to additionally modify hardware, and is also suitable for complex structures such as mitochondria and the like.

Description

Technical Field

[0001] The present invention relates to the field of optical microscope imaging technology, and in particular to a digital image processing method, a device and related components for improving STED imaging quality. Background Art

[0002] Stimulated emission depletion (STED) microscopy uses two laser beams to irradiate the sample simultaneously. One laser beam is used as excitation light to excite fluorescent molecules so that the fluorescent molecules within the focal range are in an excited state, and the other laser beam is a ring laser with zero central light intensity, which is used to suppress the fluorescence of fluorophores around the excitation focus, thereby achieving super-resolution. However, in the process of using STED microscopy, there are problems such as photobleaching, photodamage, or STED-specific background noise. The main sources of background noise include direct excitation noise (DEN) and incomplete depletion noise (IDN).

[0003] DEN is generated when fluorophores are inadvertently excited by the excitation light, and IDN is caused when fluorescence leaks in the incompletely depleted area around the ring laser. Both types of noise are affected by the STED laser intensity, so it is necessary to suppress DEN and IDN at the same time. However, the suppression method is more complicated, especially in 3D STED imaging, and the additional loss required to achieve 3D super-resolution will reduce the signal-to-background ratio (SBR). Therefore, it is necessary to reduce the STED-specific background noise to improve the image SBR and resolution.

[0004] In the prior art, there are the following methods to reduce STED specific background noise: 1. STED subtraction technology, mainly for direct excitation noise (DEN), only using the STED laser beam to scan the sample to capture background information, and then subtracting the background information from the STED image; 2. Digital enhancement, by digitally enhancing the erasing light power to improve the resolution of fluorescent beads and microtubule samples, but for complex samples (such as mitochondria), information loss will occur. However, these methods do not solve the problem of IDN. The prior art adjusts the STED intensity during imaging to suppress DEN through lifetime tuning photon separation technology, or uses the Gaussian distribution of stimulated emission double depletion technology and the second STED pulse of time-correlated detection to estimate and subtract background noise, but this method requires precise time alignment, time-correlated measurement, and careful optimization of the second STED pulse intensity and subtraction factor, which is very complicated. In addition, polarization switching can also accurately subtract STED specific background noise (including DEN and IDN). In polarization switching STED, reversing the polarization of the STED beam will produce a center-filled donut pattern, which can effectively capture and subtract background information. Dynamic polarization switching can quickly reduce background noise. However, existing methods for reducing background noise in STED microscopy either have difficulty processing complex structures such as mitochondria or require additional hardware modifications and are complex to operate. Summary of the invention

[0005] The embodiments of the present invention provide a digital image processing method, device and related components for improving the quality of STED imaging, aiming to solve the problem that existing methods for reducing background noise of STED microscopes are difficult to process complex structures such as mitochondria or require additional hardware modifications, resulting in complex operations.

[0006] In a first aspect, an embodiment of the present invention provides a digital image processing method for improving STED imaging quality, comprising:

[0007] The fluorescent beads were imaged using a STED microscope to obtain the initial STED image and the co-aggregation image;

[0008] Performing a first image processing on the initial STED image and the co-aggregation image to obtain an actual loss light image, and performing Fourier transform processing and interception processing on the actual loss light image to obtain an FFT image;

[0009] Compare the FFT image with the ideal loss light FFT image, and determine whether the FFT image has noise. If noise exists, perform threshold processing on the FFT image, and then perform inverse Fourier transform on the threshold-processed FFT image to obtain the ideal loss light image;

[0010] The initial STED image and the ideal loss light image are subjected to a second image processing to obtain a final STED image.

[0011] In a second aspect, an embodiment of the present invention provides a digital image processing device for improving STED imaging quality, comprising:

[0012] An imaging unit, used for imaging the fluorescent beads using a STED microscope to obtain an initial STED image and a co-aggregation image;

[0013] A first processing unit is used to perform a first image processing on the initial STED image and the co-aggregation image to obtain an actual loss light image, and perform Fourier transform processing and interception processing on the actual loss light image to obtain an FFT image;

[0014] A noise judgment unit, used for comparing the FFT image with the ideal loss light FFT image, and judging whether the FFT image has noise, and if noise exists, performing threshold processing on the FFT image, and then performing inverse Fourier transform on the threshold processed FFT image to obtain the ideal loss light image;

[0015] The second processing unit is used to perform a second image processing on the initial STED image and the ideal loss light image to obtain a final STED image.

[0016] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the digital image processing method as described above when executing the computer program.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the digital image processing method as described above is implemented.

[0018] The embodiment of the present invention provides a digital image processing method, device and related components for improving the quality of STED imaging. The processing method uses a STED microscope to image fluorescent beads, performs a first image processing, Fourier transform processing and interception processing on the imaged image to obtain an FFT image, and then performs threshold processing, inverse Fourier transform and a second image processing on the noisy FFT image to obtain a final STED image, which has reduced background noise and improved image resolution. The processing method of this embodiment is simple, does not require additional hardware modification, and is also applicable to complex structures such as mitochondria. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0020] Figure 1 A schematic flow chart of a digital image processing method for improving STED imaging quality provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of a sub-flow chart of a digital image processing method for improving STED imaging quality provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of another sub-process of a digital image processing method for improving STED imaging quality provided by an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of another sub-process of a digital image processing method for improving STED imaging quality provided by an embodiment of the present invention;

[0024] Figure 5 A schematic diagram of another sub-process of a digital image processing method for improving STED imaging quality provided by an embodiment of the present invention;

[0025] Figure 6 A schematic diagram of another sub-process of a digital image processing method for improving STED imaging quality provided by an embodiment of the present invention;

[0026] Figure 7 A simulation schematic diagram of a digital image processing method for improving STED imaging quality provided by an embodiment of the present invention;

[0027] Figure 8 A schematic block diagram of a digital image processing device for improving STED imaging quality provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0030] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0031] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0032] See also Figure 1 , an embodiment of the present invention provides a digital image processing method for improving STED imaging quality, comprising steps S10-S40:

[0033] S10, using STED microscopy to image the fluorescent beads to obtain an initial STED image and a co-aggregation image;

[0034] In this step, fluorescent beads are usually tiny particles labeled with fluorescent dyes, which can emit fluorescence under a STED microscope. The STED microscope works through two main laser beams, one is an excitation beam for exciting fluorescent molecules, and the other is an inhibition beam (STED beam, also known as a ring beam) for returning some fluorescent molecules to the ground state through stimulated radiation. By using a STED microscope, only the fluorescent molecules in the area located in the center of the excitation spot can emit fluorescence, thereby achieving high-resolution imaging.

[0035] When the fluorescent beads are imaged using a STED microscope, a high-resolution STED image (i.e., an initial STED image) can be obtained by adjusting the parameters of the excitation beam and the STED beam. In this image, the edges and internal structure of the fluorescent beads are very clear, because the STED technology can effectively suppress the fluorescent signal in the surrounding area and highlight the details of the central area. Then adjust the settings of the STED microscope or use different imaging modes, and then use a laser beam to excite the fluorescent molecules to obtain a co-aggregation image. In this image, the overall shape and position of the fluorescent beads will be displayed, but the details and resolution are not as clear as the initial STED image. In this embodiment, the fluorescent beads are imaged by a STED microscope to obtain an initial STED image and a confocal image, respectively. These two images provide different information about the structure and properties of the fluorescent beads for subsequent implementation processes.

[0036] S20, performing a first image processing on the initial STED image and the co-aggregation image to obtain an actual loss light image, and performing Fourier transform processing and interception processing on the actual loss light image to obtain an FFT image;

[0037] In this step, in the process of imaging the fluorescent beads using a STED microscope, the STED beam will reduce the luminous area of ​​the effective fluorescence, thereby improving the resolution. However, excessive power of the STED beam or other factors will cause light loss, resulting in a decrease in imaging quality, such as reduced image resolution, image blur or reduced contrast. Therefore, it is necessary to perform a first image processing on the initial STED image and the co-aggregated image to obtain an actual loss light image, and then perform Fourier transform processing on the actual loss light image. Among them, the Fourier transform processing is to convert the spatial domain (or time domain) signal of the actual loss light image into a frequency domain signal using a Fourier transform algorithm, thereby obtaining the frequency domain of the actual loss light image. In the frequency domain, the distribution of different frequencies in the actual loss light image can be observed, such as low-frequency distribution and high-frequency distribution, and then the frequency of interest is intercepted. The purpose of interception is to remove unnecessary frequency components, thereby obtaining an FFT image.

[0038] In one embodiment, if Figure 2 As shown, S20 includes:

[0039] S21, comparing the initial STED image and the co-aggregation image to obtain a comparison result;

[0040] S22. If the comparison result shows that there is a difference, subtract the co-aggregation image from the initial STED image to obtain an actual loss light image.

[0041] In this embodiment, in S21, the initial STED image and the co-aggregated image need to be preprocessed first, and the preprocessing process includes denoising and image registration, wherein denoising, that is, removing background noise in the image, can be removed by means of a filter; for image registration, since the initial STED image and the co-aggregated image may come from different imaging systems or different imaging conditions, it is necessary to align the two by image registration technology to ensure their consistency in spatial position, and then compare the initial STED image and the co-aggregated image to determine whether the two are consistent in terms of resolution, size, pixel depth, etc. If they are inconsistent, it means that there is a difference. The grayscale value or fluorescence intensity of the initial STED image and the co-aggregated image can also be compared pixel by pixel, and then the difference between the two images is calculated. The difference can be quantified by indicators such as mean square error (MSE) and structural similarity (SSIM). According to the comparison result, the degree of difference between the two images is recorded. If the degree of difference exceeds a set threshold (such as MSE is greater than a certain value or SSIM is less than a certain value), it is considered that there is a difference.

[0042] In S22, using image processing software or written code, pixel-by-pixel subtraction is performed between the initial STED image and the co-aggregation image that have differences.

[0043] In a specific implementation, the actual loss light image can also be verified to ensure that it reflects the difference between the initial STED image and the co-aggregation image, and a visualization tool (such as an image viewer) can be used to check whether the actual loss light image contains the expected loss light information.

[0044] In one embodiment, if Figure 3 As shown, S20 also includes:

[0045] S23, performing Fourier transform processing on the actual loss light image to obtain an actual loss light FFT image;

[0046] S24, using image software to intercept an image region of a predetermined amplitude value in the actual loss light FFT image to obtain an FFT image.

[0047] In this embodiment, in S23, the actual loss light image is imported into image processing software that supports Fourier transform processing, such as MATLAB, Python (using NumPy and SciPy libraries) or professional image processing software (such as ImageJ, Photoshop plug-in, etc.), and then the function of performing Fourier transform is selected. For a two-dimensional image, a two-dimensional FFT function is usually used. The software will calculate the FFT of the actual loss light image and generate an actual loss light FFT image, which shows the representation of the actual loss light image in the frequency domain, where the high-frequency components are located near the center of the image and the low-frequency components are located at the edge of the image.

[0048] In S24, the predetermined amplitude value can be determined based on the amplitude of the noise in the actual loss light FFT image, the amplitude range of the signal of interest, etc., and a selection tool (such as a rectangular selection box, a circular selection box, etc.) is used to intercept the image area of ​​the predetermined amplitude value in the actual loss light FFT image, which may be an area with an amplitude value greater than, less than or equal to the predetermined amplitude value, thereby obtaining an FFT image, wherein the FFT image includes amplitude information and phase information, and these two information can be used for subsequent noise judgment.

[0049] In a specific implementation, a mask technique can also be used to intercept an image region of a predetermined amplitude value in the actual loss light FFT image, and the image region of the predetermined amplitude value is used as a region of interest. A two-dimensional array of the same size as the actual loss light FFT image is created as a mask. In the mask, the element value of the region of interest is set to 1, and the element values ​​of other regions are set to 0. The mask is multiplied element by element with the actual loss light FFT image, and the resulting image is the FFT image.

[0050] S30, comparing the FFT image with the ideal loss light FFT image, and determining whether the FFT image has noise; if noise exists, performing threshold processing on the FFT image, and then performing inverse Fourier transform on the threshold processed FFT image to obtain the ideal loss light image;

[0051] In this step, the ideal loss light FFT image is a predefined, noise-free FFT image representing the ideal loss light distribution. The FFT image is compared with the ideal loss light FFT image to determine whether the FFT image has noise. The noise may appear as high-frequency components or low-frequency components, randomly distributed bright spots or dark spots, and areas significantly different from the ideal image. If noise exists, denoising is required. Denoising method This step uses threshold processing. According to the result of noise detection, a suitable threshold can be set. This threshold can be global (applicable to the entire FFT image) or local (different thresholds are set according to different areas of the image). Then, the pixels with amplitude values ​​lower or higher than the threshold are set to zero or other processing is performed (such as removal, retention or reduction of amplitude, etc.) until there is no noise in the FFT image. Then, the thresholded FFT image is inverse Fourier transformed to obtain the ideal loss light image.

[0052] In one embodiment, if Figure 4 As shown, S30 includes:

[0053] S31, obtaining an amplitude value of the FFT image, and calculating a frequency spectrum of the FFT image according to the amplitude value;

[0054] S32, comparing the frequency spectrum of the FFT image with the frequency spectrum of the ideal loss light FFT image, and determining whether a specific area in the frequency spectrum of the FFT image is 0; if not, confirming that noise exists in the FFT image.

[0055] This embodiment is a first noise judgment method, that is, obtaining the amplitude values ​​of the FFT image, which represent the intensity of the FFT image at different frequency components. According to the amplitude values, the spectrum of the FFT image is calculated, which is usually displayed as a chart showing the relationship between the frequency and the corresponding amplitude value. Then, the spectrum of the ideal loss light FFT image is obtained, and the spectrum represents the frequency domain characteristics of the loss light generated by the system in the absence of a fluorescent signal. The spectrum of the ideal loss light FFT image in a specific area is 0, and this specific area is the "donut" area of ​​the central area, that is, the ideal loss light FFT image will form an area similar to a "donut" pattern in the central area. The spectrum of the FFT image is compared with the spectrum of the ideal loss light FFT image to determine whether the position of the specific area in the FFT image corresponds to the spectrum of the position of the specific area of ​​the ideal loss light FFT image. If it is 0, it is confirmed that the FFT image has no noise. If it is not 0, it is confirmed that the FFT image has noise. Subsequent operations are performed until the FFT image has no noise.

[0056] In one embodiment, if Figure 5 As shown, S30 also includes:

[0057] S33, obtaining phase information of the FFT image, and calculating a power spectrum of the FFT image according to the phase information;

[0058] S34. Determine whether one or more values ​​in the power spectrum are 0. If not, confirm that noise exists in the FFT image.

[0059] This embodiment is a second noise judgment method, that is, obtaining the phase information of the FFT image, which represents the phase offset of each frequency component relative to a reference point, and calculating the power spectrum of the FFT image based on the phase information.

[0060] Use the amplitude and phase information of the FFT image to calculate the power spectrum, and then determine whether there is noise in the FFT image. Before making the judgment, a reasonable power threshold needs to be set. This is because in actual calculations, due to the precision limitations of numerical calculations, the values ​​in the power spectrum are unlikely to be completely 0. Therefore, the power threshold should be set to a sufficiently small positive number to distinguish between the non-zero power caused by noise and the tiny values ​​caused by calculation precision. The entire power spectrum is traversed, and the power value corresponding to each frequency component is checked. Then the noise is judged. If the power value of one or more frequency components is found in the power spectrum to be greater than the threshold (that is, not 0, in the sense of the threshold), it is considered that there is noise in the FFT image. This is because noise usually generates non-zero power on multiple frequency components. Subsequent operations are performed until there is no noise in the FFT image.

[0061] In one embodiment, if Figure 6 As shown, S30 also includes:

[0062] S35, acquiring a previous FFT image, and filtering the FFT image to obtain a filtered FFT image;

[0063] S36, judging whether there is noise in the filtered FFT image, if there is noise, continuing to filter the current FFT image, if there is no noise, performing inverse Fourier transform on the current FFT image to obtain an ideal loss light image.

[0064] In this embodiment, the previous FFT image is a previous version of the current FFT image obtained from the previous processing step. Specifically, if the current FFT image is not filtered, the previous FFT image is the original FFT image (i.e., the first round of FFT image). If the current FFT image is filtered, the previous FFT image is the FFT image after the previous round of filtering. Among them, the filtering methods include low-pass filtering, high-pass filtering, band-pass filtering, and band-stop filtering. For reducing high-frequency noise, low-pass filtering can be selected, that is, filtering the part of the acquired FFT image below the predetermined frequency value to obtain the filtered FFT image, and judging whether the filtered FFT image has noise. If the filtered FFT image still contains noise and the noise level is higher than the acceptable threshold, it is considered that the filtering effect is not ideal, and the filtering threshold is continued to be adjusted, and filtering is continued until the noise in the filtered FFT image has been reduced to an acceptable level. Then, the current FFT image is subjected to an inverse Fourier transform, and the filtered FFT image is converted back to a time domain image using the IFFT algorithm, that is, the complex amplitude value of the FFT image is inversely transformed to obtain an ideal loss light image.

[0065] S40, performing a second image processing on the initial STED image and the ideal loss light image to obtain a final STED image.

[0066] In this step, the ideal loss light image represents the loss light distribution generated by the STED microscope system in the absence of the target fluorescence signal. Then, the initial STED image and the ideal loss light image are preprocessed. The preprocessing process includes image registration. The initial STED image and the ideal loss light image are automatically aligned using an image registration algorithm to ensure that they are completely matched in space. This includes operations such as translation, rotation, and scaling to eliminate any possible displacement or deformation. Then, the two images are subjected to a second image processing. The second image processing can be performed by image subtraction. The specific process of image subtraction includes: subtracting the ideal loss light image from the initial STED image to obtain the final STED image. In the image processing software or programming environment, each pixel value of the initial STED image is subtracted from the pixel value of the ideal loss light image at the corresponding position. The difference image obtained is the final STED image. The final STED image represents the distribution of the target fluorescence signal after the loss light signal is removed.

[0067] The various images obtained by the entire processing method can be referred to Figure 7 As shown, Figure 7 In the figure, the STED donut in Figure A represents a STED image in which the specific area is not zero, the FFT in Figure A represents the non-zero area within the red dotted circular outline of the specific area, the Ideal donut in Figure B represents an ideal loss light image in which the specific area is zero (similar to a "donut" pattern), the FFT in Figure B represents the zero area within the red dotted circular outline of the specific area, the Confocal in Figure C is a confocal image after fluorescent beads imaging, the STED in Figure D is the initial STED image after fluorescent beads imaging, the Donutbeam in Figure E is the actual loss light image, the FFT in Figure E is the FFT image intercepted by the actual loss light (Nonzero means that the specific area of ​​the intercepted FFT image is not zero), the Ideal donut beam in Figure F represents the ideal loss light image, the Filtered FFT in Figure F represents the filtered FFT image (Almost zero means that the specific area of ​​the filtered FFT image is almost zero), the STED in Figure G is the initial STED image after fluorescent beads imaging, and the Redepleted STED in Figure G is the final STED image.

[0068] In specific implementation, the processing method can be used to achieve the improvement of the resolution and signal-to-background ratio of the dual organelle structure, that is, the STED images of the Golgi apparatus and the nuclear pore complex are processed, that is, the STED images are deconvoluted using the Huygens software, and the ring light image is obtained by the confocal image and the STED image, and then the ring light image is digitally enhanced, that is, multiplied by a digital power enhancement factor (K), at which time the power of the ring light is digitally increased by K times, and then the confocal image is subtracted from the digitally enhanced ring light to obtain the final STED image, and then the image resolution of the final STED image is calculated using the Fourier ring, and the resolution is increased from the original 70nm to 35nm, and this method also improves the signal-to-background ratio of the STED image of the dual organelle structure.

[0069] In addition, the processing method can also improve the resolution and signal-to-background ratio of 3D-STED images. That is, the 3D-STED image of the fluorescent beads is subjected to a series of processing in the aforementioned embodiment, and then image verification can be performed, and it is found that the final STED image significantly eliminates background noise, and the resolution in the zy direction is characterized by the half-height full width, and the z-axis resolution is improved from 150nm to 100nm.

[0070] The embodiment of the present invention further provides a digital image processing device for improving the quality of STED imaging, which is used to execute any embodiment of the digital image processing method for improving the quality of STED imaging. Figure 8 , Figure 8 : is a schematic block diagram of a digital image processing device for improving STED imaging quality provided by an embodiment of the present invention. The digital image processing device 500 for improving STED imaging quality includes:

[0071] An imaging unit 510 is used to image the fluorescent beads using a STED microscope to obtain an initial STED image and a co-aggregation image;

[0072] A first processing unit 520 is used to perform a first image processing on the initial STED image and the co-aggregation image to obtain an actual loss light image, and perform Fourier transform processing and truncation processing on the actual loss light image to obtain an FFT image;

[0073] A noise determination unit 530 is used to compare the FFT image with the ideal loss light FFT image and determine whether the FFT image has noise. If noise exists, threshold processing is performed on the FFT image, and then inverse Fourier transform is performed on the threshold-processed FFT image to obtain an ideal loss light image.

[0074] The second processing unit 540 is configured to perform a second image processing on the initial STED image and the ideal loss light image to obtain a final STED image.

[0075] In one embodiment, the first processing unit 520 is configured to:

[0076] Comparing the initial STED image and the co-aggregation image to obtain a comparison result;

[0077] If the comparison result shows that there is a difference, the initial STED image is subtracted from the co-aggregation image to obtain an actual loss light image.

[0078] In one embodiment, the first processing unit 520 is further configured to:

[0079] Performing Fourier transform processing on the actual loss light image to obtain an actual loss light FFT image;

[0080] An image region of a predetermined amplitude value in the actual loss light FFT image is intercepted by using image software to obtain an FFT image.

[0081] In one embodiment, the noise determination unit 530 is used to:

[0082] Acquire the amplitude value of the FFT image, and calculate the frequency spectrum of the FFT image according to the amplitude value;

[0083] The frequency spectrum of the FFT image is compared with the frequency spectrum of the ideal loss light FFT image to determine whether a specific area in the frequency spectrum of the FFT image is 0. If not, it is confirmed that noise exists in the FFT image.

[0084] In one embodiment, the noise determination unit 530 is further configured to:

[0085] Obtaining phase information of the FFT image, and calculating a power spectrum of the FFT image according to the phase information;

[0086] It is determined whether one or more values ​​in the power spectrum are 0. If not, it is determined that noise exists in the FFT image.

[0087] In one embodiment, the noise determination unit 530 is further configured to:

[0088] Acquire a previous FFT image, and filter the FFT image to obtain a filtered FFT image;

[0089] It is determined whether there is noise in the filtered FFT image. If there is noise, the current FFT image continues to be filtered. If there is no noise, the current FFT image is inversely Fourier transformed to obtain an ideal loss light image.

[0090] In one embodiment, the second processing unit 540 is configured to:

[0091] The ideal loss light image is subtracted from the initial STED image to obtain a final STED image.

[0092] An embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the digital image processing method as described in the above embodiment when executing the computer program.

[0093] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the digital image processing method as described in the above embodiment is implemented.

[0094] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the above-described equipment, devices and units can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. Those of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0095] In the several embodiments provided by the present invention, it should be understood that the disclosed equipment, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. Units with the same function may also be combined into one unit. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices or units, or may be an electrical, mechanical or other form of connection.

[0096] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0097] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), disk or optical disk and other media that can store program codes.

[0099] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A digital image processing method for improving the quality of STED imaging, characterized in that: include: The fluorescent beads were imaged using a STED microscope to obtain the initial STED image and the co-aggregation image; Performing a first image processing on the initial STED image and the co-aggregation image to obtain an actual loss light image, and performing Fourier transform processing and interception processing on the actual loss light image to obtain an FFT image; Compare the FFT image with the ideal loss light FFT image, and determine whether the FFT image has noise. If noise exists, perform threshold processing on the FFT image, and then perform inverse Fourier transform on the threshold-processed FFT image to obtain the ideal loss light image; The initial STED image and the ideal loss light image are subjected to a second image processing to obtain a final STED image.

2. The digital image processing method according to claim 1, characterized in that: The first image processing is performed on the initial STED image and the co-aggregation image to obtain an actual loss light image, comprising: Comparing the initial STED image and the co-aggregation image to obtain a comparison result; If the comparison result shows that there is a difference, the initial STED image is subtracted from the co-aggregation image to obtain an actual loss light image.

3. The digital image processing method according to claim 1, characterized in that: The performing Fourier transform processing and interception processing on the actual loss light image to obtain an FFT image includes: Performing Fourier transform processing on the actual loss light image to obtain an actual loss light FFT image; An image region of a predetermined amplitude value in the actual loss light FFT image is intercepted by using image software to obtain an FFT image.

4. The digital image processing method according to claim 3, characterized in that: The determining whether the FFT image has noise includes: Acquire the amplitude value of the FFT image, and calculate the frequency spectrum of the FFT image according to the amplitude value; The frequency spectrum of the FFT image is compared with the frequency spectrum of the ideal loss light FFT image to determine whether a specific area in the frequency spectrum of the FFT image is 0. If not, it is confirmed that noise exists in the FFT image.

5. The digital image processing method according to claim 3, characterized in that: The determining whether the FFT image has noise includes: Acquire phase information of the FFT image, and calculate the power spectrum of the FFT image according to the phase information; It is determined whether one or more values ​​in the power spectrum are 0. If not, it is determined that noise exists in the FFT image.

6. The digital image processing method according to claim 3, characterized in that: If noise exists, threshold processing is performed on the FFT image, and then the threshold-processed FFT image is inversely Fourier transformed to obtain an ideal loss light image, including: Acquire a previous FFT image, and filter the FFT image to obtain a filtered FFT image; It is determined whether there is noise in the filtered FFT image. If there is noise, the current FFT image continues to be filtered. If there is no noise, the current FFT image is inversely Fourier transformed to obtain an ideal loss light image.

7. The digital image processing method according to claim 1, characterized in that: The performing a second image processing on the initial STED image and the ideal loss light image to obtain a final STED image comprises: The ideal loss light image is subtracted from the initial STED image to obtain a final STED image.

8. A digital image processing device for improving STED imaging quality, used to implement the digital image processing method for improving STED imaging quality according to any one of claims 1 to 7, characterized in that: include: An imaging unit, used for imaging the fluorescent beads using a STED microscope to obtain an initial STED image and a co-aggregation image; A first processing unit is used to perform a first image processing on the initial STED image and the co-aggregation image to obtain an actual loss light image, and perform Fourier transform processing and interception processing on the actual loss light image to obtain an FFT image; A noise judgment unit, used for comparing the FFT image with the ideal loss light FFT image, and judging whether the FFT image has noise, and if noise exists, performing threshold processing on the FFT image, and then performing inverse Fourier transform on the threshold processed FFT image to obtain the ideal loss light image; The second processing unit is used to perform a second image processing on the initial STED image and the ideal loss light image to obtain a final STED image.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the digital image processing method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the digital image processing method according to any one of claims 1 to 7 is implemented.

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