CN114463198A - Method for improving fluorescent imaging definition - Google Patents
Method for improving fluorescent imaging definition Download PDFInfo
- Publication number
- CN114463198A CN114463198A CN202111680424.3A CN202111680424A CN114463198A CN 114463198 A CN114463198 A CN 114463198A CN 202111680424 A CN202111680424 A CN 202111680424A CN 114463198 A CN114463198 A CN 114463198A
- Authority
- CN
- China
- Prior art keywords
- signal
- definition
- fluorescence imaging
- sharpness
- sequence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 76
- 238000012632 fluorescent imaging Methods 0.000 title description 2
- 238000003384 imaging method Methods 0.000 claims abstract description 34
- 238000000799 fluorescence microscopy Methods 0.000 claims abstract description 32
- 238000001228 spectrum Methods 0.000 claims abstract description 29
- 238000012545 processing Methods 0.000 claims abstract description 15
- 238000009499 grossing Methods 0.000 claims abstract description 11
- 230000003595 spectral effect Effects 0.000 claims abstract description 9
- 230000008569 process Effects 0.000 claims description 17
- 238000013139 quantization Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000005070 sampling Methods 0.000 claims description 6
- 239000013589 supplement Substances 0.000 claims description 3
- 238000002073 fluorescence micrograph Methods 0.000 claims 1
- 239000000203 mixture Substances 0.000 claims 1
- 239000002994 raw material Substances 0.000 claims 1
- 238000011160 research Methods 0.000 abstract description 7
- 238000004458 analytical method Methods 0.000 abstract description 6
- 238000004590 computer program Methods 0.000 description 5
- 238000001514 detection method Methods 0.000 description 5
- 230000005283 ground state Effects 0.000 description 5
- 230000005281 excited state Effects 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 230000005855 radiation Effects 0.000 description 3
- 230000007704 transition Effects 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000005284 excitation Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 230000003321 amplification Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 229930002875 chlorophyll Natural products 0.000 description 1
- 235000019804 chlorophyll Nutrition 0.000 description 1
- ATNHDLDRLWWWCB-AENOIHSZSA-M chlorophyll a Chemical compound C1([C@@H](C(=O)OC)C(=O)C2=C3C)=C2N2C3=CC(C(CC)=C3C)=[N+]4C3=CC3=C(C=C)C(C)=C5N3[Mg-2]42[N+]2=C1[C@@H](CCC(=O)OC\C=C(/C)CCC[C@H](C)CCC[C@H](C)CCCC(C)C)[C@H](C)C2=C5 ATNHDLDRLWWWCB-AENOIHSZSA-M 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- GNBHRKFJIUUOQI-UHFFFAOYSA-N fluorescein Chemical compound O1C(=O)C2=CC=CC=C2C21C1=CC=C(O)C=C1OC1=CC(O)=CC=C21 GNBHRKFJIUUOQI-UHFFFAOYSA-N 0.000 description 1
- 239000007850 fluorescent dye Substances 0.000 description 1
- 230000009545 invasion Effects 0.000 description 1
- 238000004020 luminiscence type Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000010363 phase shift Effects 0.000 description 1
- 230000010287 polarization Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000003325 tomography Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10064—Fluorescence image
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
The invention discloses a method for improving the definition of fluorescence imaging, which comprises the steps of solving the power spectrum of the smooth processing of a received imaging signal and calculating the definition; setting a threshold value according to the definition to identify a conventional imaging signal, and searching a smoothed power spectrum peak if the conventional imaging signal cannot be identified; if the spectral peak is 2, 2FSK is obtained, and if the spectral peak is not 2, the conventional imaging signal is subjected to square processing and the power spectrum is subjected to smoothing processing; and fitting instantaneous frequency by using a least square method, carrying out local denoising, and outputting an imaging signal with higher definition. According to the method, the signal of the fluorescence imaging is specially processed to obtain a better frequency signal, namely, higher definition is obtained, complex operation is reduced, and the method can be well applied to plant stress analysis and research.
Description
Technical Field
The invention relates to the technical field of plant stress analysis and fluorescence imaging definition, in particular to a method for improving fluorescence imaging definition.
Background
Fluorescence is a common luminescence phenomenon in nature, and is generated by the interaction of photons with molecules, and the interaction process can be described by a Jablonslc molecular energy diagram: most molecules are in the lowest vibration energy level So of the ground state in the normal state, when excited by energy (optical energy, electric energy, chemical energy and the like), electrons around atomic nuclei transit from the ground state energy level So to an excited state (a first or second excited state) with higher energy, the electrons in the excited state are in a high-energy state, are unstable and release energy to return to the ground state through two paths, one is radiation transition (including fluorescence and phosphorescence processes) releasing energy in the form of photons, and the other is non-radiation transition releasing energy in the form of thermal energy and the like; generally, after an electron outside an atomic nucleus is excited to transit from a ground state So to an excited state Si, the electron rapidly drops to a lowest vibration level through a non-radiative transition mode, and then returns to the ground state from the lowest vibration level, energy is released in the form of photon radiation, and emergent light with the property is called fluorescence.
The theoretical basis of fluorescence imaging is that the intensity of a fluorescence signal emitted by a fluorescent substance after being excited has a linear relationship with the amount of fluorescein in a certain range, and the fluorescence imaging system comprises a fluorescence signal excitation system (an excitation light source and a light path transmission assembly), a fluorescence signal collection assembly and a signal detection and amplification system (CCD and PMT).
At present, the significance of plant stress analysis on agriculture and environmental ecology research is far away, polarization imaging and plant chlorophyll fluorescence imaging detection have the advantages of non-invasion and no damage, so that the method for researching and analyzing plant stress through a fluorescence imaging technology is a good method, but the method has no high definition, the prior art focuses more on research and biological cells, so that in the research of plant stress on agriculture and environmental ecology research, people pay little attention, and the fluorescence imaging distance is far from ultra-high definition, which also brings great challenges to the work of plant stress researchers.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: there is no higher definition fluorescence imaging device for plant stress analysis.
In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of obtaining a power spectrum of the received imaging signal smooth processing, and calculating definition; setting a threshold value according to the definition to identify a conventional imaging signal, and searching a smoothed power spectrum peak if the conventional imaging signal cannot be identified; if the spectral peak is 2, the frequency shift keying (2 FSK) is obtained, and if the spectral peak is not 2, the conventional imaging signal is subjected to square processing and the power spectrum is subjected to smoothing processing; and fitting instantaneous frequency by using a least square method, carrying out local denoising, and outputting an imaging signal with higher definition.
As a preferable aspect of the method for improving the sharpness of fluorescence imaging according to the present invention, wherein: calculating the definition comprises smoothing the received imaging signal by using an index L-Z complexity strategy for describing sequence characteristics to obtain a power spectrum;
defining { s (k) } as the amplitude spectrum of the signal, k ═ 1,2, …, N as the signal data length, and quantizing and encoding { s (k) }.
As a preferable aspect of the method for improving the sharpness of fluorescence imaging according to the present invention, wherein: also comprises the following steps of (1) preparing,
setting the quantization level as L, letting a ═ max { s (k) } represent the maximum value of the signal amplitude spectrum, dividing { s (k) } into L layers in the interval of (0, a ], then:
where { r (k) } denotes a number sequence of L symbols after quantization { s (k) }, and j is the definition.
As a preferable aspect of the method for improving the sharpness of fluorescence imaging according to the present invention, wherein: setting the threshold includes calculating a maximum value of the smoothed spectrum { s (k) }, and setting the threshold to be threshold max (, s (k)/2).
As a preferable aspect of the method for improving the sharpness of fluorescence imaging according to the present invention, wherein: the squaring process may include the steps of,
adding assignment into an empty generation pool initially, defining existing symbol strings in the empty generation pool without loss of generality, wherein the assignment is completed by adding operation;
let P ═ r (1) r (2) … r (l), Q ═ r (l +1), determine whether Q can be copied from POv, i.e., whether Q is a substring in PQv, where PQv represents a string resulting from concatenating P, Q together and deleting the last character.
As a preferable aspect of the method for improving the sharpness of fluorescence imaging according to the present invention, wherein: also comprises the following steps of (1) preparing,
if copying is possible, P is kept unchanged, and Q continuously supplements one symbol, namely Q is r (l +1) r (l + 2);
if copying is impossible, adding Q to the generation pool, wherein P is r (1) r (2) … r (l) r (l +1), and Q is r (l + 2);
and circularly iterating until the generation pool contains all reconstruction sequences, counting the times C of the adding operation, namely the complexity of the L-Z, and adding 1 to the C if the last step of operation is copying.
As a preferable aspect of the method for improving the sharpness of fluorescence imaging according to the present invention, wherein: the smoothing process may include the steps of,
in order to avoid noise interference, firstly, carrying out smooth search processing on the imaging signal to obtain a smooth frequency spectrum { s (k) };
subtracting threshold value from each number of S (k) to obtain a new sequence S*(k)}；
From the sequence S*(k) Starting from the first position k being 1, the value S of the current position k is determined*(k) Whether less than 0;
if the current position k is less than 0, the numerical value S of the current position k is determined*(k) Set 0 until k-step is cut off, sequence S*(k) The values in the } are updated;
let initial spectral peak number p be 0, search sequence { S*(k) H, if the value S of the current position k is*(k) A value S greater than 0 and at the position k-1*(k-1) is less than 0, then p is added to 1, the sequence { S*(k) Traversing one time;
judging the size of p, if p is 2, indicating that the signal is the conventional imaging signal, otherwise, indicating that the signal is other signals.
As a preferable aspect of the method for improving the sharpness of fluorescence imaging according to the present invention, wherein: fitting the instantaneous frequency using a least squares method includes,
using fitted lines and variance of instantaneous frequency of signal
To determine whether the instantaneous frequency is a straight line, as follows,
wherein f (n) represents the instantaneous frequency of the signal,
represents a least squares fitted line, alpha represents a fitted line
The slope of (a), beta represents the intercept of the fitting straight line, alpha and beta are parameters to be estimated, and the fitting straight line is obtained by calculating alpha and beta.
As a preferable aspect of the method for improving the sharpness of fluorescence imaging according to the present invention, wherein: the local denoising comprises reading in local discharge signal data from a database to obtain sampling information; selecting proper Eps values and Minpts values by using the sampling information and carrying out density clustering calculation on the partial discharge signal data; and classifying the partial discharge signal data by using a density clustering strategy and removing discontinuous partial discharge signals as noise points.
As a preferable aspect of the method for improving the sharpness of fluorescence imaging according to the present invention, wherein: the density clustering calculation comprises the steps of selecting a data point x from the partial discharge signal data and checking an Eps neighborhood of the data point x; if the data point x is a core point and is not assigned to a certain class, finding out all points with reachable density to form a class containing the data point x; if the data point x is not the core point, the data point x is labeled as the noise point; and (5) circularly iterating until all the points are processed.
The invention has the beneficial effects that: according to the method, the signal of the fluorescence imaging is specially processed to obtain a better frequency signal, namely, higher definition is obtained, complex operation is reduced, and the method can be well applied to plant stress analysis and research.
Drawings
FIG. 1 is a schematic flow chart of a method for improving the sharpness of fluoroscopic imaging according to an embodiment of the present invention;
FIG. 2 is a schematic view of three different illuminations for a method of improving the sharpness of fluorescence imaging according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present application, "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that, in the present invention, "a plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that A, B, C all comprise, "comprises A, B or C" means comprise one of A, B, C, "comprises A, B and/or C" means comprise any 1 or any 2 or 3 of A, B, C.
It should be understood that in the present invention, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, and B can be determined from a. Determining B from a does not mean determining B from a alone, but may be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Example 1
Referring to fig. 1 and 2, a first embodiment of the present invention provides an implementation of a method for improving the sharpness of fluorescence imaging, specifically including:
s1; and calculating the power spectrum of the received imaging signal in a smoothing way and calculating the definition. It should be noted that calculating the sharpness includes:
smoothing the received imaging signal by using an index L-Z complexity strategy for describing sequence characteristics to obtain a power spectrum;
defining { s (k) } as an amplitude spectrum of the signal, k ═ 1,2, …, N, and N as a signal data length, and carrying out quantization coding on { s (k) };
setting the quantization level as L, letting a ═ max { s (k) } represent the maximum value of the signal amplitude spectrum, dividing { s (k) } into L layers in the interval of (0, a ], then:
where { r (k) } denotes a number sequence of L symbols after quantization { s (k) }, and j is the definition.
S2: and (4) setting a threshold value according to the definition to identify the conventional imaging signal, and searching the smoothed power spectrum peak if the conventional imaging signal cannot be identified. It should be noted that the setting of the threshold includes:
the maximum value of the smoothed spectrum { s (k) } is calculated, and the threshold value is set to be maximum max (s (k))/2.
The identified portion of the operating code is illustrated as follows:
s3: if the peak of the spectrum is 2, the peak is 2FSK, and if the peak of the spectrum is not 2, the square processing and the smoothing processing are carried out on the conventional imaging signal and the power spectrum. It should be further noted that the squaring process includes:
adding assignment into the empty generation pool initially, defining existing symbol strings in the empty generation pool without loss of generality, and completing assignment by adding operation;
let P ═ r (1) r (2) … r (l), Q ═ r (l +1), judge whether Q can be copied from POv, i.e. whether Q is a substring in PQv, where PQv represents a string obtained by splicing P, Q together and deleting the last character;
if copying is possible, P is kept unchanged, and Q continuously supplements one symbol, namely Q is r (l +1) r (l + 2);
if copying is impossible, adding Q to the generation pool, wherein P is r (1) r (2) … r (l) r (l +1), and Q is r (l + 2);
and circularly iterating until the generation pool contains all reconstruction sequences, counting the times C of the adding operation, namely the complexity of the L-Z, and adding 1 to the C if the last step of operation is copying.
Further, the smoothing process includes:
in order to avoid noise interference, firstly, carrying out smooth search processing on an imaging signal to obtain a smooth frequency spectrum { s (k) };
subtracting threshold from each number of S (k) to obtain a new sequence S*(k)}；
From the sequence S*(k) Starting from the first position k being 1, the value S of the current position k is determined*(k) Whether less than 0;
if the current position k is less than 0, the numerical value S of the current position k is determined*(k) Set 0 until k-step is cut off, sequence S*(k) The values in the } are updated;
let initial spectral peak number p be 0, search sequence { S*(k) H, if the value S of the current position k is*(k) A value S greater than 0 and at the position k-1*(k-1) is less than 0, then p is added to 1, the sequence { S*(k) Traversing one time;
judging the size of p, if p is 2, indicating that the signal is a normal imaging signal, otherwise, indicating that the signal is other signals.
S4: and fitting instantaneous frequency by using a least square method, carrying out local denoising, and outputting an imaging signal with higher definition. It should be further noted that the fitting of the instantaneous frequency by the least square method includes:
using fitted lines and variance of instantaneous frequency of signal
To determine whether the instantaneous frequency is a straight line, as follows,
wherein f (n) represents the instantaneous frequency of the signal,
represents a least squares fitted line, alpha represents a fitted line
The slope of (a), beta represents the intercept of the fitting straight line, alpha and beta are parameters to be estimated, and the fitting straight line is obtained by calculating alpha and beta.
Further, the local denoising includes:
reading partial discharge signal data from a database to obtain sampling information;
selecting proper Eps values and Minpts values by utilizing sampling information and carrying out density clustering calculation on the data of the local discharge signals;
and classifying the partial discharge signal data by using a density clustering strategy and removing discontinuous partial discharge signals as noise points.
Specifically, the density clustering calculation includes:
selecting a data point x from the partial discharge signal data, and checking an Eps neighborhood of the data point x;
if the data point x is a core point and is not assigned to a certain class, finding out all the points with reachable density to form a class containing the data point x;
if the data point x is not a core point, the data point x is marked as a noise point;
and (5) circularly iterating until all the points are processed.
Referring to fig. 2, there are three different illumination detection technology fields of optical tomography, specifically including:
(1) time domain TD: when independent measurements of tissue absorption, scattering or lifetime of fluorescent dyes are required, TD or FD techniques must be used. Using ultrafast (femtosecond to picosecond) photon pulses to irradiate the tissue and time-resolving the arrival of photons at different locations around the tissue boundary according to time, they can use early arriving photons to improve resolution compared to the continuous wave method because highly diffuse photons are rejected, disadvantageously TD methods are less sensitive than CW methods due to the lower duty cycle (i.e. the length of time the laser beam and detector are turned on) that results in lower average light intensity available for imaging; furthermore, TD instruments are more noisy than CW systems due to the time and intensity fluctuations associated with ultra-fast switching electronics and pulsed lasers.
(2) Frequency domain FD: light of modulated intensity at frequency f is used which creates a wavelet of light at the same frequency in the diffusion medium. Measurements of light intensity and phase shift of the photon wavefront far from the light source or excited fluorochromes reveal information about tissue optical properties and the biodistribution of fluorochromes, FD methods are less affected by ambient light than CW and TD methods, but they require several hundred MHz or higher to achieve CW resolution enhancement, they are also less reliable than continuous wave methods because of reduced signal-to-noise ratio detection when sensing high frequencies, data obtained at multiple frequencies can improve FD imaging performance, and can be equivalent to TD data by inverse fourier transformation.
(3) Continuous wave domain CW: optimal signal-to-noise performance using constant intensity light, simple and low cost optical components. CW light sources and detectors are generally more stable and have lower noise characteristics than those used in TD and FD methods; the disadvantages are as follows: the problems of tissue absorption and inability to image fluorescence lifetime due to scattering are difficult to solve.
Preferably, the method obtains a better frequency signal by specially processing the signal of the fluorescence imaging, namely obtaining higher definition, reduces complicated operations, and can be well applied to plant stress analysis and research.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of improving the sharpness of a fluorescence image, comprising: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
obtaining a power spectrum of the received imaging signal smooth processing, and calculating definition;
setting a threshold value according to the definition to identify a conventional imaging signal, and searching a smoothed power spectrum peak if the conventional imaging signal cannot be identified;
if the spectral peak is 2, the frequency shift keying (2 FSK) is obtained, and if the spectral peak is not 2, the conventional imaging signal is subjected to square processing and the power spectrum is subjected to smoothing processing;
and fitting instantaneous frequency by using a least square method, carrying out local denoising, and outputting an imaging signal with higher definition.
2. A method of improving sharpness of fluorescence imaging according to claim 1, wherein: the calculating of the sharpness includes calculating a sharpness including,
smoothing the received imaging signal by using an index L-Z complexity strategy for describing sequence characteristics to obtain a power spectrum;
defining { s (k) } as an amplitude spectrum of the signal, k 1,2, …, N as a signal data length, and performing quantization coding on { s (k) }.
3. A method of improving sharpness of fluorescence imaging according to claim 2, wherein: also comprises the following steps of (1) preparing,
setting the quantization level as L, letting a ═ max { s (k) } represent the maximum value of the signal amplitude spectrum, dividing { s (k) } into L layers in the interval of (0, a ], then:
where { r (k) } denotes a number sequence of L symbols after quantization { s (k) }, and j is the definition.
4. A method of improving sharpness of fluorescence imaging according to claim 1 or 3, wherein: setting the threshold value includes calculating a maximum value of the smoothed spectrum { s (k) }, and setting the threshold value as threshold max (s (k)/2).
5. A method of improving sharpness of fluorescence imaging according to claim 4, wherein: the squaring process may include the steps of,
adding assignment into an empty generation pool initially, defining existing symbol strings in the empty generation pool without loss of generality, wherein the assignment is completed by adding operation;
let P ═ r (1) r (2) … r (l), Q ═ r (l +1), determine whether Q can be copied from POv, i.e., whether Q is a substring in PQv, where PQv represents a string resulting from concatenating P, Q together and deleting the last character.
6. A method of improving sharpness of fluorescence imaging according to claim 5, wherein: also comprises the following steps of (1) preparing,
if copying is possible, P is kept unchanged, and Q continuously supplements one symbol, namely Q is r (l +1) r (l + 2);
if copying is impossible, adding Q to the generation pool, wherein P is r (1) r (2) … r (l) r (l +1), and Q is r (l + 2);
and circularly iterating until the generation pool contains all reconstruction sequences, counting the times C of the adding operation, namely the complexity of the L-Z, and adding 1 to the C if the last step of operation is copying.
7. A method of improving sharpness of fluorescence imaging according to claim 5, wherein: the smoothing process may include the steps of,
in order to avoid noise interference, firstly, carrying out smooth search processing on the imaging signal to obtain a smooth frequency spectrum { s (k) };
subtracting threshold value from each number of S (k) to obtain a new sequence S*(k)}；
From the sequence S*(k) Starting from the first position k being 1, the value S of the current position k is determined*(k) Whether less than 0;
if the current position k is less than 0, the numerical value S of the current position k is determined*(k) Set 0 until k-step is cut off, sequence S*(k) The values in the } are updated;
let initial spectral peak number p be 0, search sequence { S*(k) H, if the value S of the current position k is*(k) Greater than 0 and the value S of the position k-1*(k-1) is less than 0, then p is added to 1, the sequence { S*(k) Traversing one time;
judging the size of p, if p is 2, indicating that the signal is the conventional imaging signal, otherwise, indicating that the signal is other signals.
8. A method of improving sharpness of fluorescence imaging according to claim 7, wherein: fitting the instantaneous frequency using a least squares method includes,
using fitted lines and variance of instantaneous frequency of signal
To determine whether the instantaneous frequency is a straight line, as follows,
wherein f (n) represents the instantaneous frequency of the signal,
represents a least squares fitted line, alpha represents a fitted line
The slope of (a), beta represents the intercept of the fitting straight line, alpha and beta are parameters to be estimated, and the fitting straight line is obtained by calculating alpha and beta.
9. A method of improving sharpness of fluorescence imaging according to claim 8, wherein: the local de-noising includes the steps of,
reading partial discharge signal data from a database to obtain sampling information;
selecting proper Eps values and Minpts values by using the sampling information and carrying out density clustering calculation on the partial discharge signal data;
and classifying the partial discharge signal data by using a density clustering strategy and removing discontinuous partial discharge signals as noise points.
10. A method of improving sharpness of fluorescence imaging according to claim 9, wherein: the density cluster calculation includes the calculation of a density cluster,
selecting a data point x from the partial discharge signal data, and checking an Eps neighborhood of the data point x;
if the data point x is a core point and is not assigned to a certain class, finding out all points with reachable density to form a class containing the data point x;
if the data point x is not the core point, the data point x is labeled as the noise point;
and (5) circularly iterating until all the points are processed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111680424.3A CN114463198A (en) | 2021-12-30 | 2021-12-30 | Method for improving fluorescent imaging definition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111680424.3A CN114463198A (en) | 2021-12-30 | 2021-12-30 | Method for improving fluorescent imaging definition |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114463198A true CN114463198A (en) | 2022-05-10 |
Family
ID=81406916
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111680424.3A Pending CN114463198A (en) | 2021-12-30 | 2021-12-30 | Method for improving fluorescent imaging definition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114463198A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116843582A (en) * | 2023-08-31 | 2023-10-03 | 南京诺源医疗器械有限公司 | Denoising enhancement system and method of 2CMOS camera based on deep learning |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102542539A (en) * | 2011-12-30 | 2012-07-04 | 四川大学 | Strong-applicability image enhancement method based on power spectrum analysis |
| CN103969031A (en) * | 2014-05-14 | 2014-08-06 | 中国科学院长春光学精密机械与物理研究所 | Method for measuring response matrix of liquid crystal corrector through least square method |
| CN109247951A (en) * | 2018-09-18 | 2019-01-22 | 北京工业大学 | A kind of sub- diameter imaging method of ultrasonic scattering based on power spectrum |
| US20190310198A1 (en) * | 2016-12-22 | 2019-10-10 | Yonsei University Industry Foundation (Yonsei Uif) | Fluorescence lifetime measurement apparatus and method capable of finding two or more fluorescence lifetime components by computing least square error through virtual fluorescence distribution model from signal collected in analog mean delay method |
| CN112022109A (en) * | 2020-11-06 | 2020-12-04 | 南京诺源医疗器械有限公司 | Medical fluorescence imaging image data acquisition system and acquisition method thereof |
| CN113608189A (en) * | 2021-07-19 | 2021-11-05 | 南京誉葆科技有限公司 | Radar signal modulation mode identification method based on spectrum complexity |
-
2021
- 2021-12-30 CN CN202111680424.3A patent/CN114463198A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102542539A (en) * | 2011-12-30 | 2012-07-04 | 四川大学 | Strong-applicability image enhancement method based on power spectrum analysis |
| CN103969031A (en) * | 2014-05-14 | 2014-08-06 | 中国科学院长春光学精密机械与物理研究所 | Method for measuring response matrix of liquid crystal corrector through least square method |
| US20190310198A1 (en) * | 2016-12-22 | 2019-10-10 | Yonsei University Industry Foundation (Yonsei Uif) | Fluorescence lifetime measurement apparatus and method capable of finding two or more fluorescence lifetime components by computing least square error through virtual fluorescence distribution model from signal collected in analog mean delay method |
| CN109247951A (en) * | 2018-09-18 | 2019-01-22 | 北京工业大学 | A kind of sub- diameter imaging method of ultrasonic scattering based on power spectrum |
| CN112022109A (en) * | 2020-11-06 | 2020-12-04 | 南京诺源医疗器械有限公司 | Medical fluorescence imaging image data acquisition system and acquisition method thereof |
| CN113608189A (en) * | 2021-07-19 | 2021-11-05 | 南京誉葆科技有限公司 | Radar signal modulation mode identification method based on spectrum complexity |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116843582A (en) * | 2023-08-31 | 2023-10-03 | 南京诺源医疗器械有限公司 | Denoising enhancement system and method of 2CMOS camera based on deep learning |
| CN116843582B (en) * | 2023-08-31 | 2023-11-03 | 南京诺源医疗器械有限公司 | Denoising enhancement system and method of 2CMOS camera based on deep learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Röntsch et al. | Constraining couplings of top quarks to the Z boson in $$ t\overline {t} $$+ Z production at the LHC | |
| Chen et al. | An adaptive denoising method for Raman spectroscopy based on lifting wavelet transform | |
| Caravaca et al. | Characterization of water-based liquid scintillator for Cherenkov and scintillation separation | |
| Pfender et al. | Single-spin stochastic optical reconstruction microscopy | |
| JP6389763B2 (en) | System and method for time-resolved fluorescence imaging and pulse shaping | |
| RU2469637C2 (en) | Method for optical imaging interior of turbid medium, method for reconstruction of image of interior of turbid medium, apparatus for imaging interior of turbid medium, medical imaging device and computer program product for use in said methods and devices | |
| Barnes et al. | Ho: Ho upconversion: applications to Ho lasers | |
| Leung et al. | Effects of incomplete decay in fluorescence lifetime estimation | |
| JP2013246140A (en) | Information processing apparatus, information processing method and program | |
| CN107220625B (en) | LIBS spectrum automatic identification and classification method for sample | |
| Liu et al. | Infrared PINEM developed by diffraction in 4D UEM | |
| Koong et al. | Coherence in cooperative photon emission from indistinguishable quantum emitters | |
| CN104871294A (en) | Defect detection in saturable absorbers | |
| CN114463198A (en) | Method for improving fluorescent imaging definition | |
| Liu et al. | Continuous background correction using effective points selected in third-order minima segments in low-cost laser-induced breakdown spectroscopy without intensified CCD | |
| Vicidomini et al. | Automatic deconvolution in 4Pi-microscopy with variable phase | |
| CN115997115A (en) | Methods and systems for characterizing spill-over diffusion in flow cytometer data | |
| O’Connell et al. | Photon-counting, energy-resolving and super-resolution phase contrast X-ray imaging using an integrating detector. | |
| JP2017528705A (en) | Method for correcting infrared absorption spectra | |
| JP2017528705A5 (en) | ||
| Conwell et al. | Efficient automated algorithm for the sizing of dielectric microspheres using the resonance spectrum | |
| Dai et al. | Heavy quark jet production near threshold | |
| Shultzman et al. | Enhanced imaging using inverse design of nanophotonic scintillators | |
| Andersson et al. | Parametrization of energy sharing distributions in direct double photoionization of He | |
| Pu et al. | Spectral-resolved cone-beam X-ray luminescence computed tomography with principle component analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |