Imaging method of low-pass response system based on point spread function quasi-pulsation

Abstract

The present invention relates to the field of image processing and imaging technology, and specifically to a low-pass response system imaging method based on point spread function quasi-pulsation, comprising: obtaining the spectrum of the point spread function according to the autocorrelation function of the output image of the low-pass response system; designing the spectrum of the desired output point spread function; applying quasi-pulsation processing to the imaging result, and improving the resolution of the image by adjusting the width of the point spread function. The present invention proposes a new imaging data processing method for a low-pass response imaging system to reduce the width of the point spread function, improve the resolution, and thus improve the image clarity and detail recognizability.

Description

Technical Field

[0001] The invention relates to the technical field of image processing and imaging, and in particular to a low-pass response system imaging method based on point spread function quasi-pulse. Background Art

[0002] Detection technology has extensive and extremely important applications in human life and production. Detection and research of targets by emitting radiation wave signals from a signal source is generally called active detection, which is the most important and critical method of detection technology. Detection and research of targets by receiving the target's own radiation wave signals without emitting radiation signals is called passive detection.

[0003] Whether it is an active detection method or a passive detection method, the detection accuracy and detection range of each detection technology are limited by its own physical principles. The limiting resolution after the target is imaged is a key indicator to measure the detection accuracy of a detection technology, and it is also an important issue faced by each detection technology. The limiting resolution problem is essentially a low-pass response imaging problem, that is, in the detection imaging results, the high-frequency components are missing to a certain extent, and the target details are lost. This kind of detection imaging system with limiting resolution is generally referred to as a low-pass response imaging system. Synthetic aperture radar (SAR), sonar (SONAR, SAS), optical microscope, medical imaging (MRI, CT, X-Ray), astronomical observation imaging, telescopes, etc., all belong to low-pass response imaging systems.

[0004] The limiting resolution (or resolution) of a low-pass response imaging system is generally described by the point spread function (PSF) of the imaging system, which is the response of the imaging system to the target point source (or point object) (i.e., the image of the point source). When the observation method of the imaging system (such as the characteristics of the emitted radiation signal source, the signal receiving method, etc.) is determined, its resolution is basically determined. Subsequent imaging signal processing plays a key role in improving imaging accuracy.

[0005] In order to more clearly distinguish the detection target, the resolution of the imaging system must be improved. Changing the point spread function characteristics of the imaging system through subsequent signal processing is an effective method.

[0006] In view of this, the present invention proposes a low-pass response system imaging method based on point spread function quasi-pulsation. Summary of the invention

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a low-pass response system imaging method based on point spread function quasi-pulsation, comprising: obtaining the spectrum of the point spread function according to the autocorrelation function of the output image of the low-pass response system; designing the spectrum of the expected output point spread function; applying quasi-pulsation processing to the imaging results, and improving the resolution of the image by adjusting the width of the point spread function.

[0008] Preferably, let the target object in the spatial domain be d(r), the point spread function PSF be p(r), and the image be x(r), then x(r)=d(r)*p(r);

[0009] Assume that the frequency domain of the target object in the spatial domain is D(k), the frequency domain of the point spread function is P(k), and the frequency domain of the image is X(k), then X(k) = D(k)P(k);

[0010] The point spread function is a zero-phase function, and the spectrum P(k) of the point spread function is obtained from the image autocorrelation function;

[0011] Let e ​​be the pulse coefficient, e is a positive number > 1, and the expected output transfer function spectrum after broadening is P e (k), P e (k)=P(k / e), where k represents the spatial frequency variable in the frequency domain.

[0012] Assume that the image spectrum after the point spread function is quasi-pulsed is Y(k), then Y(k) = D(k)P e (k);

[0013] According to D(k) and P e (k) calculation method, transform Y(k) to get:

[0014] Y(k)=X(k)P(k / e) / P(k);

[0015] Y(k) is inverse Fourier transformed into the time-space domain to obtain the pulsed image signal y(r) of the point spread function.

[0016] Preferably, there are multiple ways to take the value of the pulse coefficient e, and different values ​​are assigned to the pulse coefficient e to obtain different results of the spectrum broadening degree of the transfer function in different spatial frequency directions.

[0017] Preferably, the point spread function spectrum P(k) is calculated by the image autocorrelation function, a single one-dimensional data z(r1) is intercepted along any calibration direction of the acquired image, and the autocorrelation calculation of the one-dimensional function is performed on z(r1) to obtain the autocorrelation function data;

[0018] Perform autocorrelation calculation on all one-dimensional image data z(r2),z(r3)...,z(rn) in the calibration direction;

[0019] The autocorrelation functions of all one-dimensional functions are summed to obtain the total autocorrelation function z(r), and z(r) is Fourier transformed and calculated to obtain the point spread function spectrum P(k).

[0020] Preferably, the image point spread function is quasi-pulsed, the frequency spectrum P(k) of the point spread function calculated by the autocorrelation function is substituted into Y(k)=X(k)P(k / e) / P(k), and the imaging data is quasi-pulsed to obtain a higher resolution imaging processing result.

[0021] A low-pass response system imaging system based on point spread function quasi-pulsation, which is implemented based on the low-pass response system imaging method based on point spread function quasi-pulsation, including: a spectrum acquisition module, a spectrum design module and an imaging processing module;

[0022] The spectrum acquisition module obtains the spectrum of the point spread function according to the autocorrelation function of the output image of the low-pass response system;

[0023] The spectrum design module is used to design the spectrum of the desired output point spread function;

[0024] The imaging processing module applies a pseudo-pulse process to the imaging result, and improves the resolution of the image by adjusting the width of the point spread function.

[0025] Beneficial effects of the present invention: The present invention proposes a new imaging data processing method for a low-pass response imaging system to reduce the point spread function width and improve the resolution, thereby improving image clarity and detail recognizability.

[0026] The present invention proposes the concept of quasi-pulsation of point spread function for the first time, proposes pulse coefficient variables and a method for determining the same, and determines a method and steps for implementing the quasi-pulsation of point spread function.

[0027] The point spread function quasi-pulse processing provided by the present invention reduces the width of the point spread function, improves the resolution of the imaging result, and can identify smaller target shapes.

[0028] The desired output point spread function of the present invention is obtained by moderately pulsing the original point spread function, avoiding the influence of high-frequency noise. The quasi-pulsation process can well maintain the original signal-to-noise ratio of the imaging data while improving the resolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] 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 only 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 creative labor.

[0030] in:

[0031] Figure 1 It is a flow chart of a low-pass response system imaging method based on point spread function quasi-pulsation;

[0032] Figure 2 is the point spread function (width) graph of the low-pass response imaging system;

[0033] Figure 3 It is the point spread function (narrow) graph of the low-pass response imaging system;

[0034] Figure 4 It is a forward diagram of the linear formation process of the image of the wide point spread function;

[0035] Figure 5 It is a forward diagram of the linear formation process of the image of the narrow point spread function;

[0036] Figure 6 It is a comparison diagram of the target point source, the original point spread function and the expected output point spread function;

[0037] Figure 7 This is the result of the point spread function quasi-pulse processing of the point target model data;

[0038] Figure 8 It is the SAR radar 2D image direction definition diagram;

[0039] Fig. 9 It is a schematic diagram of SAR radar observation mode;

[0040] Fig.10 It is a schematic diagram of the side scan observation plane (perpendicular to the movement direction of the aircraft carrier platform);

[0041] Fig.11 It is the real part diagram of the pulse beam signal received by the radar - linear frequency modulation signal;

[0042] Fig.12 It is the signal diagram after pulse compression (matched filtering);

[0043] Fig.13 is a plot of the azimuth Doppler shift versus the radial velocity of the target relative to the sensor;

[0044] Fig.14It is a single target SAR radar 2D (range-azimuth) signal diagram;

[0045] Fig.15 This is the result of applying the pseudo-pulse method to the SAR radar 1-point spread function;

[0046] Fig.16 This is a partial enlarged diagram of the result of applying the quasi-pulse of the SAR radar 1-point spread function;

[0047] Fig.17 This is the result of applying the pseudo-pulse of the 2-point spread function of the SAR radar;

[0048] Fig.18 This is a partial enlarged diagram of the result of applying the quasi-pulse of the SAR radar 2-point spread function;

[0049] Fig.19 This is the result of applying the pseudo-pulse of the SAR radar three-point spread function;

[0050] Fig. 20 This is a partial enlarged diagram of the result of the pseudo-pulse application of the SAR radar three-point spread function;

[0051] Fig.21 This is the result of applying the quasi-pulse point spread function to the SAS radar example;

[0052] Fig. 22 This is a schematic diagram of Airy Disk;

[0053] Fig.23 It is a schematic diagram of Rayleigh Criterion;

[0054] Fig.24 It is a comparison diagram between the point spread function quasi-pulse processing result and the SIM microscope observation result;

[0055] Fig.25 This is the result of the quasi-pulse processing of the point spread function of the neuron cell;

[0056] Fig.26 This is the result of the quasi-pulse processing of the point spread function of human embryonic stem cells;

[0057] Fig. 27 This is a partial enlarged view of the result of the quasi-pulse processing of the point spread function of human embryonic stem cells;

[0058] Fig.28 This is the result of pseudo-pulse processing of microtubule point spread function in melanoma cells;

[0059] Fig.29 It is the result of applying the quasi-pulse method to the point spread function of cardiac MRI imaging;

[0060] Fig.30 This is a local enlargement of the image 1 of the application result of the quasi-pulse application of the point spread function of cardiac MRI imaging;

[0061] Fig.31 Figure 2 is a local magnification of the image result of applying the quasi-pulse point spread function to cardiac MRI imaging;

[0062] Fig.32 X-Ray imaging example (cat) Point spread function quasi-pulse application result diagram. DETAILED DESCRIPTION

[0063] In order to make the above-mentioned purposes, features and advantages of the present invention more understandable, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0064] Example 1

[0065] See also Figures 1 to 7 , which is the first embodiment of the present invention, provides a low-pass response system imaging method based on point spread function quasi-pulsation.

[0066] S1: A brief introduction to the basic concepts of low-pass response imaging system.

[0067] S101: Define point spread function and frequency domain transfer function.

[0068] Ideally, every infinitesimal point in the object should be an infinitesimal image point after being imaged by the imaging system; in fact, the image of a small point obtained by any imaging system will expand (and become blurred) to varying degrees. The point spread function is a description of the degree of blur of the small point imaged by the imaging system.

[0069] The point spread function (PSF) is the response of an imaging system to a point source or point object (i.e., the image of a point source), or is called the impulse response of the system. PSF is the resolution of the imaging system.

[0070] The imaging system point spread function (PSF) is the spatial domain form of the frequency domain transfer function (TF). A narrow point spread function in the spatial domain (high resolution) corresponds to a wide transfer function in the frequency domain, and vice versa.

[0071] By narrowing the point spread function or widening the transfer function spectrum, the resolution of the imaging result can be improved.

[0072] Figure 2 , Figure 3 The 1D and 2D diagrams are respectively the waveforms of the wide point spread function (narrow frequency domain transfer function), the narrow point spread function (wide frequency domain transfer function), and the frequency domain amplitude spectrum. Figure 2 and Figure 3 It can be seen from the figure that the point spread function has an expansion effect on the target point source. The wider the point spread function is (the narrower the frequency domain transfer function is), the greater the blurriness of the image of the target point source is.

[0073] S102: Linear systems theory describing the image formation process.

[0074] The image formation process is described as linear by linear systems theory, that is, when multiple objects are imaged simultaneously, the result is equal to the sum of the independent images of the individual objects.

[0075] The image of a complex object can be regarded as the convolution of the real object and PSF. Suppose the target object in the spatial domain is d(r), the point spread function PSF is p(r), and the image is x(r), then x(r)=d(r)*p(r).

[0076] Figure 4 Forward modeling of the image formation process for the wide point spread function, Figure 4 The middle left part is the real object distribution map, and there are two point sources very close to each other in the center. Figure 4 The middle right part is the imaging result obtained by forward modeling. Since the width of the point spread function is greater than the distance between the two point sources in the center, the resolution of the imaging result is low and the two point sources cannot be distinguished.

[0077] Figure 5 Forward simulation of the image formation process of a narrow point spread function. The width of the point spread function is smaller than the distance between the two central point sources, and the resolution is higher, and the two point sources can be basically distinguished.

[0078] S2: Principle and implementation of point spread function quasi-pulsing technology for low-pass response imaging system.

[0079] S201: Principle of point spread function quasi-pulsing technology for low-pass response imaging system.

[0080] The large width of the point spread function (narrow transfer function spectrum) of the low-pass response imaging system is the fundamental reason for the low resolution of the imaging results. Broadening the transfer function spectrum and thus reducing the width of the point spread function is the key to solving the problem.

[0081] The basic principle of the point spread function quasiperitization technology for low-pass response imaging systems is to obtain the spectrum of the point spread function based on the image autocorrelation function, design the expected output point spread function spectrum with a wider transfer function spectrum, perform point spread function quasiperitization processing on the imaging results to narrow the point spread function, and achieve the purpose of improving the resolution of the imaging results.

[0082] Figure 6 This is a comparison diagram of the target point source, the original point spread function, and the expected output point spread function. Figure 6 The middle figures (a), (b) and (c) are the 2D graphs, 1D waveforms and amplitude spectra of the target point source, the original point spread function and the expected output point spread function, respectively.

[0083] The target object can be regarded as a full-band data. As the image of the convolution result of the target and the point spread function, its spectrum must be basically consistent with the spectrum of the transfer function. Therefore, the spectrum of the stretched image is equivalent to the spectrum of the stretched transfer function.

[0084] It is very difficult to completely pulse the point spread function (and turn it into an infinitely small point with completely clear edges), and sidelobe artifacts may appear. At the same time, the imaging results often have heavier noise in the high-frequency band. Complete pulse will enhance the high-frequency noise and reduce the signal-to-noise ratio. Moderate pulse (i.e., quasi-pulse) is easier to implement and more stable, which is conducive to maintaining the signal-to-noise ratio of the image.

[0085] S202: Method and steps for implementing point spread function quasi-pulse technology.

[0086] Assuming that the frequency domain of the target object in the spatial domain is D(k), the frequency domain of the point spread function is P(k), and the frequency domain of the image is X(k), then X(k)=D(k)P(k).

[0087] Since the point spread function is a zero-phase function, the spectrum P(k) of the point spread function can be obtained from the image autocorrelation function.

[0088] Let e ​​be the pulse coefficient (e is a positive number > 1), and the expected output transfer function spectrum after broadening is P e (k):

[0089] P e (k) = P(k / e);

[0090] Assume that the image spectrum after the point spread function is quasi-pulsed is Y(k), then Y(k) = D(k)P e (k).

[0091] According to D(k) and P e (k) calculation method, transform Y(k) to get:

[0092] Y(k)=X(k)P(k / e) / P(k);

[0093] Y(k) is inverse Fourier transformed into the time-space domain to obtain the pulsed image signal y(r) of the point spread function.

[0094] S203: Taking different values ​​for the pulse coefficient e.

[0095] If e=2, the spectrum is broadened to twice the original spectrum (theoretically the resolution is doubled).

[0096] In 2D and 3D situations, different e values ​​can be taken in different spatial frequency directions to obtain results with different degrees of spectrum broadening of the transfer function in different spatial frequency directions (different degrees of pseudo-pulsation in different spatial directions).

[0097] S204: A method for calculating the point spread function spectrum P(k) using the image autocorrelation function.

[0098] A single one-dimensional data z(r1) is intercepted along a certain direction of the acquired image, and the autocorrelation calculation of the one-dimensional function is performed on z(r1) to obtain the autocorrelation function data.

[0099] Autocorrelation calculation is performed on all one-dimensional image data z(r2), z(r3)..., z(rn) in this direction.

[0100] The autocorrelation functions of all one-dimensional functions are summed to obtain the total autocorrelation function z(r), and z(r) is Fourier transformed and calculated to obtain the point spread function spectrum P(k).

[0101] S205: Pseudo-pulse processing of the image point spread function.

[0102] The image point spread function is quasi-pulsed, and the frequency spectrum P(k) of the point spread function calculated by the autocorrelation function is substituted into Y(k)=X(k)P(k / e) / P(k). The imaging data is subjected to quasi-pulsed point spread function processing to obtain higher resolution imaging processing results.

[0103] S3: Application analysis of point spread function quasi-pulse technology.

[0104] Figure 7 It is the result of quasi-pulse processing of the point spread function of the point target model data.

[0105] Figure 7The middle figure (a) is the model forward modeling result of the wide point spread function, and the figure (b) is the point spread function quasi-pulse processing result of the model forward modeling data (e=2). Due to the low resolution of the imaging result, the width of the point spread function is greater than the distance between the two point sources in the center, and the two point sources that are close to the center of the image cannot be distinguished. After the point spread function is quasi-pulsed (e=2), the point spread function becomes narrower, the resolution is improved, and the two points that are close to the center of the image can basically be distinguished.

[0106] Example 2

[0107] See also Figures 8 to 20 , which is the second embodiment of the present invention, provides the application effect of a low-pass response system imaging method based on point spread function quasi-pulsation on the principle of limiting resolution and imaging of synthetic aperture radar SAR.

[0108] S1: Principle of the limiting resolution of synthetic aperture radar SAR.

[0109] S101: SAR radar 2D image direction definition and SAR radar observation method.

[0110] like Figure 8 As shown, the X direction is the range direction, i.e., the side-view direction of the radar beam. The Y direction is the azimuth direction, i.e., the navigation direction of the radar carrier platform (such as an airplane, a satellite).

[0111] Fig. 9 This is a schematic diagram of the SAR radar observation method. While the radar carrier platform flies at a constant speed in the navigation direction, the radar transmits pulse beam signals in the range-looking direction (i.e., the side-looking direction, left or right) at a certain time interval (while receiving the pulse beam signals reflected from the ground surface), completing the acquisition of 2D images of the ground surface with a certain observation band width in the range direction and a certain observation length in the azimuth direction.

[0112] S102: The contradiction between the signal-to-noise ratio and resolution of SAR radar signals.

[0113] In any detection system, the received pulse signal must have strong enough energy and high enough resolution to effectively measure the observed target.

[0114] Fig.10 Schematic diagram of the side scan observation plane (perpendicular to the movement direction of the carrier platform).

[0115] If the duration of the transmitted pulse signal is T (i.e., pulse start edge - pulse end edge), two targets with a distance greater than T in the echo signal can be clearly distinguished. The time resolution of the system signal before compression processing is the time length T of the signal. The longer T is, the stronger the energy is, but the lower the time resolution is.

[0116] S103: Pulse compression (matched filtering) of linear frequency modulation signal and range resolution formula.

[0117] The pulse beam signal emitted by the radar is generally a linear frequency modulation signal with a certain duration (see Fig.11 ) to ensure the average transmission power of the pulse signal (to improve the signal-to-noise ratio of the observation data) and the bandwidth (to improve the range resolution).

[0118] Based on the phase modulation of the linear frequency modulation signal, the received stretched pulse signal is subjected to pulse compression (also called matched filtering) processing to achieve signal compression and obtain the required resolution.

[0119] Implementation of matched filtering: In the time domain, it is achieved by cross-correlating the received signal with the conjugate of the expected linear frequency modulation signal; in the frequency domain, it is achieved by multiplying the signal spectrum with the expected linear frequency modulation signal spectrum containing the quadratic conjugate phase (quadratic phase cancellation).

[0120] Range resolution: After matched filtering (see Fig.12 ), the distance resolution (dimension is time) (-3dB width) is:

[0121] ρ=0.886 / (|K|T);

[0122] Where T is the pulse signal time length (s); K is the linear modulation frequency (hz / s).

[0123] The pulse compression signal obtained by matched filtering is similar to the point spread function of the aforementioned low-pass response imaging system. The point spread function quasi-pulse technology can be used to process SAR radar signals to improve the spatial resolution of the data in the range direction.

[0124] S104: The concept of synthetic aperture and the generation of SAR radar azimuth phase modulation.

[0125] Pulse compression in the range direction is achieved based on phase modulation of the linear frequency modulation signal. The phase modulation of the signal in the azimuth direction comes from the movement of the radar platform. As the radar platform moves, a target on the ground will be illuminated by multiple pulses during the radar illumination time. Signal processing processes a group of signals to form an equivalent antenna with a very long aperture, that is, a synthetic aperture.

[0126] The frequency shift caused by the relative motion between the target and the radar platform in the azimuth direction is called the Doppler frequency. The Doppler frequency is proportional to the radial velocity of the target relative to the sensor. The relative radial velocity of the target and the radar platform is different when the relative position is different (the center of the scene η c 0), resulting in a change in Doppler frequency, which in turn produces a chirp signal in the azimuth direction similar to that in the range direction (see Fig.13 ).

[0127] S105: constructing an azimuth matched filter and an azimuth resolution formula.

[0128] The directional resolution before SAR radar signal processing is the true aperture radar resolution, that is, the projection of the beam width on the ground:

[0129] ρ a ′=[0.886R(η c )λ] / L a ;

[0130] Among them, L a is the radar antenna length (m); R(η c ) is the scene center skew moment (m).

[0131] Based on the linear frequency modulation characteristics of the azimuth Doppler frequency, the received signal is subjected to matched filtering processing in the azimuth direction similar to that in the range direction to achieve azimuth signal compression and obtain a higher resolution signal similar to that in the range direction.

[0132] Fig.14 This is a diagram of the 2D (range-azimuth) signal of a single target SAR radar. Fig.14 The pulse signal in the figure has been range matched filtered, but has not been corrected for range motion (the relative positions of the target and the platform are different, and the distance between them is different, which causes range motion. Before range motion correction, the arrival time of the azimuth pulse signal is hyperbolic, and after range motion correction, the arrival time of the azimuth pulse signal is constant). Azimuth matched filtering is performed after range motion correction is completed.

[0133] After azimuth matched filtering, through mathematical derivation and approximate calculation, the SAR azimuth resolution (the dimension is distance) is:

[0134] ρ a =L a / 2;

[0135] The signal waveform after azimuth matched filtering is similar to that after range matched filtering. Point spread function quasi-pulse technology can also be used to process azimuth signals to improve the spatial resolution of data in azimuth.

[0136] S2: Application effect of synthetic aperture radar SAR imaging.

[0137] Figures 15 to 20 The result images and local magnifications of the point spread function quasi-pulse application of three SAR examples ( Fig.16 , Fig.18 , Fig. 20 They are Fig.15 , Fig.17 , Fig.19(a partial enlargement of the image). Fig.15 , Fig.17 , Fig.19 In the figure, (a) is the original image, and (b) is the pseudo-pulse result of the point spread function.

[0138] After the point spread function is quasi-pulsed, the image resolution is significantly improved, the details are clearer, and the signal-to-noise ratio is well maintained. After the point spread function is quasi-pulsed, more ground objects can be identified and the information is richer.

[0139] Example 3

[0140] See also Fig.21 , which is the third embodiment of the present invention, provides a low-pass response system imaging method based on point spread function quasi-pulsation in the synthetic aperture sonar SAS limit resolution principle and imaging application effect.

[0141] S1: Principle of the ultimate resolution of synthetic aperture sonar (SAS).

[0142] S101: Sonar (or synthetic aperture sonar SAS) works similarly to SAR radar.

[0143] The working mode and signal processing method of SAS sonar and SAR radar are very similar. Both can perform corresponding matched filtering in the range and azimuth directions to obtain the desired resolution. The pulse compression signal obtained by matched filtering is similar to the point spread function of the low-pass response imaging system mentioned above.

[0144] S102: Point spread function quasi-pulsing technology can be fully used to process SAS sonar signals to improve the spatial resolution of SAS images in the range and azimuth directions.

[0145] S2: Synthetic aperture sonar SAS imaging application effect.

[0146] Fig.21 This is the result graph of the SAS example point spread function quasi-pulse application. Fig.21 Figure (a) is the original image, and Figure (b) is the point spread function pseudo-pulse result.

[0147] After the point spread function is quasi-pulsed, the image resolution is significantly improved, the clarity is improved to a certain extent, and the signal-to-noise ratio is well maintained.

[0148] Example 4

[0149] See also Figure 22 to Figure 28 , which is the fourth embodiment of the present invention, provides an imaging method of a low-pass response system based on point spread function quasi-pulsation and the application effect of the limiting resolution principle and imaging of a fluorescence microscope.

[0150] S1: Principle of limiting resolution of fluorescence microscopy.

[0151] According to Fraunhofer diffraction and Airy disk, when a point light source is diffraction-limited through a convex lens of finite aperture, Fraunhofer diffraction occurs due to the wave nature of light, and it cannot be focused into an infinitely small point, but forms a light spot at the focus; the center of the light spot is a bright elementary spot, surrounded by a group of weaker concentric ring stripes (Airy Pattern) alternating between light and dark.

[0152] The diffraction spot is the point spread function of the microscope imaging system. The bright spot in the center bounded by the first dark fringe is called the Airy Disk (see Fig. 22 ).

[0153] According to the resolvability criterion, the Rayleigh Criterion, each point light source will form an Airy disk on the focal plane after passing through a finite aperture convex lens. When the distance between the Airy disks formed by two point light sources on the focal plane is equal to the radius of the Airy disk (that is, when the center of one Airy disk coincides with the edge of another Airy disk), the two point light sources can be identified by the human eye or optical instruments. This criterion is called the Rayleigh Criterion (see Fig.23 ).

[0154] According to the Abbe formula for the resolution limit, the angular distance between the centers of two Airy disks that meets the Rayleigh criterion (or the angle between two point light sources and the optical center of the lens) is called the minimum resolution angle. After complex mathematical derivation, we get: Where D is the aperture of the optical instrument and λ is the wavelength of the light wave.

[0155] Helmholtz and Abbe proposed the Abbe formula for the ideal resolution limit when oblique light is illuminated: σ = 0.5λ, where σ is the distance between two point light sources.

[0156] Optical microscopy is of great significance in in vivo biological research, but it has resolution limitations.

[0157] According to Abbe's formula, shortening the wavelength can produce higher resolution. For example, the resolution of electron microscope using X-rays reaches 0.2nm, but the electron microscope will destroy the activity of cells and even change their original structure. Optical microscope can continuously track any protein molecule under living conditions, which is of great significance for biological research. In living observation, optical microscope is still irreplaceable.

[0158] The resolution limit of optical microscopes is limited to 200nm by Abbe's formula (the shortest wavelength of blue-violet light in visible light is 400nm), while many viruses, subcellular structures, organelles and biological macromolecules are smaller than 200nm, far from meeting the needs of life sciences. The limit needs to be broken.

[0159] Structured light illumination fluorescence microscopy (SIM) is a mature technology that breaks through the resolution limit of optical microscopy. SIM introduces a structured light modulator (modulation) into the illumination light path to modulate part of the target high-frequency information outside the optical transfer function (Optical TF) cutoff frequency down to the detection range of the OTF. SIM decodes the target high-frequency information through a specific reconstruction algorithm, and then expands the image high-frequency information to achieve super-resolution imaging. SIM optimizes and expands the OTF spectrum through spatial frequency modulation, which essentially broadens the optical transfer function and reduces the point spread function to achieve super-resolution imaging.

[0160] The existing reconstruction algorithms of SIM microscopes have sensitive adjustable parameters, which make it difficult to ensure the uniqueness of the reconstruction results, and thus cannot fully guarantee their reliability.

[0161] The function of point spread function quasi-pulse technology is to reduce the point spread function. When applied to the processing of fluorescence microscope images, it can effectively improve the resolution and break through the limit of the ultimate resolution to a certain extent. The algorithm of point spread function quasi-pulse technology is stable and has high reliability.

[0162] S2: Application effect of fluorescence microscope imaging.

[0163] Fig.24 This is a comparison diagram of the point spread function quasi-pulse processing results of the endoplasmic reticulum microscope image of Hela (human cervical cancer) cells and the SIM microscope image. Fig.24 The middle figure (a) is a general fluorescence microscope image, figure (b) is the result of point spread function quasi-pulse processing of the general fluorescence microscope image, and figure (c) is the observation result of SIM fluorescence microscope.

[0164] exist Fig.24 In the figure, after the point spread function of the general fluorescence microscope image is processed by quasi-pulse, the resolution is significantly improved, the cell tissue morphology is clearer, and it is very similar to the observation image of SIM fluorescence microscope, which shows that the image details obtained by point spread function quasi-pulse processing are real and reliable, and can achieve a breakthrough in the resolution limit of fluorescence microscopy to a certain extent.

[0165] Fig.25This is the result of the point spread function quasi-pulse processing of the neuron cell microscope image. The original image resolution is relatively low and the details are not clear enough. The point spread function pulse processing improves the resolution, clearly depicts more details of the neuron cells, and the image signal-to-noise ratio is also well maintained.

[0166] Fig.26 This is the result of the quasi-pulse processing of the point spread function of human embryonic stem cells. Fig. 27 for Fig.26 A local magnified image of the stem cell. After the point spread function is quasi-pulsed, more internal details of the stem cells can be seen, and the resolution is greatly improved.

[0167] Fig.28 The result of the pseudo-pulse processing of the point spread function of melanoma cell microtubules. Due to the limitation of the wavelength of the fluorescent light wave, the original microscope image cannot distinguish microtubules smaller than half the wavelength, and many tiny microtubules cannot be identified. After the point spread function pseudo-pulse processing, the resolution is improved, breaking through the limit resolution of the optical microscope to a certain extent, and more microtubules are clearly visible.

[0168] Example 5

[0169] See also Figure 29 to Figure 32 , which is the fifth embodiment of the present invention, provides an imaging method of a low-pass response system based on quasi-pulsation of a point spread function and the application effect of the limiting resolution principle and imaging of medical images.

[0170] S1: Principle of limiting resolution of medical images.

[0171] Modern medical imaging is an important basis for medical diagnosis. Medical imaging is to generate radiation waves from a wave source and transmit them to the human body in a directional manner, and then receive the radiation waves that pass through the human body to image the human body. There are many types of medical imaging, including MRI, X-Ray, CT, ultrasound, etc.

[0172] The physical properties of radiation waves (such as wavelength, bandwidth, wave speed, etc.) inevitably lead to the limit of image spatial resolution. The limit of spatial resolution is one of the reasons why medical images have a certain degree of detail invisibility (and blur). The spatial resolution of medical images determines the minimum size of lesions or human tissues that can be observed clinically.

[0173] The principle of the spatial resolution limit of medical images can still be described by the point spread function, and the point spread function quasi-pulse technology is an effective means to improve the spatial resolution of medical images.

[0174] S2: Application effects of medical imaging.

[0175] Fig.29 The application results of point spread function quasi-pulse for cardiac MRI imaging example are as follows: Fig.30and Fig.31 They are Fig.29 After point spread function quasi-pulse processing, the resolution is significantly improved, the details inside the heart tissue are increased, and the signal-to-noise ratio is maintained.

[0176] Fig.32 This is the result of applying point spread function quasi-pulsation to an X-Ray imaging example (cat). After point spread function quasi-pulsation, the resolution is significantly improved and the bone morphology is clearer.

[0177] Example 6

[0178] The sixth embodiment of the present invention is different from the previous embodiment in that:

[0179] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the 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, a server, or a network device, etc.) to perform all or part of the steps of the methods 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), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0180] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0181] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0182] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0183] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.

[0184] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0185] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A low-pass response system imaging method based on point spread function quasi-pulsation, characterized in that: include: The spectrum of the point spread function is obtained according to the autocorrelation function of the output image of the low-pass response system; Design the spectrum of the desired output point spread function; Applying quasi-pulse processing to the imaging results to improve the image resolution by adjusting the width of the point spread function; The image data captured by the low-pass response imaging system is described as linear by the linear system theory during the formation process. When multiple objects are imaged simultaneously, the imaging result is equal to the sum of the independent imaging of a single object. The image of the target object is regarded as the convolution of the real object and the point spread function PSF. Let the target object in the spatial domain be d(r), the point spread function PSF be p(r), and the image be x(r), then x(r)=d(r)*p(r); Assume that the frequency domain of the target object in the spatial domain is D(k), the frequency domain of the point spread function is P(k), and the frequency domain of the image is X(k), then X(k)=D(k)P(k); The point spread function is a zero-phase function, and the spectrum P(k) of the point spread function is obtained from the image autocorrelation function; Let e ​​be the pulse coefficient, e is a positive number > 1, and the expected output transfer function spectrum after broadening is P e (k), P e (k) = P(k / e), where k represents the spatial frequency variable in the frequency domain; Assume that the image spectrum after the point spread function is quasi-pulsed is Y(k), then Y(k) = D(k)P e (k); According to D(k) and P e (k) calculation method, transform Y(k) to get: Y(k)=X(k)P(k / e) / P(k); Inverse Fourier transform Y(k) into the time-space domain to obtain the pulsed image signal y(r) of the point spread function; There are many ways to take the value of the pulse coefficient e. By assigning different values ​​to the pulse coefficient e, different results of the spectrum broadening of the transfer function in different spatial frequency directions are obtained. The point spread function spectrum P(k) is calculated by the image autocorrelation function, a single one-dimensional data z(r1) is intercepted along any calibration direction of the acquired image, and the autocorrelation calculation of the one-dimensional function is performed on z(r1) to obtain the autocorrelation function data; Perform autocorrelation calculation on all one-dimensional image data z(r2),z(r3)...,z(rn) in the calibration direction; The autocorrelation functions of all one-dimensional functions are summed to obtain the total autocorrelation function z(r), and z(r) is Fourier transformed and calculated to obtain the point spread function spectrum P(k).

2. The low-pass response system imaging method based on point spread function quasi-pulsation according to claim 1, characterized in that: The image point spread function is quasi-pulsed, and the frequency spectrum P(k) of the point spread function calculated by the autocorrelation function is substituted into Y(k)=X(k)P(k / e) / P(k). The imaging data is subjected to quasi-pulsed point spread function processing to obtain higher resolution imaging processing results.

3. A low-pass response system imaging system based on point spread function quasi-pulsation, which is implemented based on the low-pass response system imaging method based on point spread function quasi-pulsation according to any one of claims 1 and 2, characterized in that: include: Spectrum acquisition module, spectrum design module and imaging processing module; The spectrum acquisition module obtains the spectrum of the point spread function according to the autocorrelation function of the output image of the low-pass response system; The spectrum design module is used to design the spectrum of the desired output point spread function; The imaging processing module applies a pseudo-pulse process to the imaging result, and improves the resolution of the image by adjusting the width of the point spread function.

Images