CN117635494A - Multi-reference-value confocal endoscope image correction method and related equipment - Google Patents
Multi-reference-value confocal endoscope image correction method and related equipment Download PDFInfo
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Abstract
The application discloses a multi-reference-value confocal endoscope image correction method and related equipment, and relates to the field of image processing, wherein the method comprises the following steps: acquiring a uniform scanning sampling point data set and a sinusoidal sampling data point set; calculating the center difference information of the data in the uniform scanning sampling point data set and the sinusoidal sampling data point set; calculating a reference variable based on the center difference information, the stretching coefficient, the galvanometer scanning frequency and the sine sampling frequency; determining a correction reference value based on the reference variable and the reference variable threshold information; and correcting the sinusoidal sampling data set according to the correction reference value and the central column information of the image in the sinusoidal sampling data point set.
Description
Technical Field
The present disclosure relates to the field of image processing, and more particularly, to a multi-reference confocal endoscopic image correction method and related apparatus.
Background
The confocal endoscope is medical equipment which can extend into a human body by means of channels such as a gastroscope, a colonoscope and the like to acquire local histological images so as to realize accurate diagnosis of micro focus, gastrointestinal lesions and early gastrointestinal canceration. There are two important components in the scan control module of a confocal endoscope: a resonant mirror and a galvanometer vibrating mirror. The resonant mirror acts to rapidly scan light in a horizontal direction (also referred to as an X-galvanometer mirror), and the galvanometer mirror acts to scan light in a vertical direction (also referred to as a Y-galvanometer mirror), which cooperate to obtain a two-dimensional planar image.
The resonant mirror operates on the principle of reciprocating rotation through a certain angle along a rotation axis, steering when reaching the edge of the scanning range, and rotating in the opposite direction. And the angular velocity during scanning varies sinusoidally with the spatial position. The angular velocity of the sinusoidal features during scanning is slow at the edges and fast in the middle, resulting in distortion of the whole with stretching at both ends and compression in the middle. In the related art, an accurate correction method is lacking.
Disclosure of Invention
In the summary, a series of concepts in a simplified form are introduced, which will be further described in detail in the detailed description. The summary of the present application is not intended to define the key features and essential features of the claimed subject matter, nor is it intended to be used to determine the scope of the claimed subject matter.
In a first aspect, the present application proposes a method for correcting a multi-reference confocal endoscopic image, the method comprising:
acquiring a uniform scanning sampling point data set and a sinusoidal sampling data point set;
calculating the center difference information of the data in the uniform scanning sampling point data set and the sinusoidal sampling data point set;
calculating a reference variable based on the center difference information, the stretching coefficient, the galvanometer scanning frequency and the sine sampling frequency;
determining a correction reference value based on the reference variable and the reference variable threshold information;
and correcting the sinusoidal sampling data set according to the correction reference value and the central column information of the image in the sinusoidal sampling data point set.
Optionally, the calculating the reference variable based on the center difference information, the stretching coefficient, the galvanometer scanning frequency and the sinusoidal sampling frequency includes:
the above reference variable x is calculated according to the following formula:
wherein dn 2 Is the central difference information, alpha is the stretch coefficient, f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
Optionally, the reference variable threshold information includes first threshold information and second threshold information, the second threshold information is larger than the first threshold information, the correction reference value includes a first correction reference value, a second correction reference value and a third correction reference value,
the determining a correction reference value based on the reference variable and the reference variable threshold information includes:
determining the first correction reference value as a correction reference value in the case where the reference variable is smaller than the first threshold information; and/or the number of the groups of groups,
determining the second correction reference value as a correction reference value in the case where the reference variable is greater than the second threshold information; and/or the number of the groups of groups,
and determining the third correction reference value as a correction reference value when the reference variable is greater than or equal to the first threshold information and less than or equal to the second threshold information.
Optionally, the method further comprises:
determining the first correction reference value dn based on 11 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
Optionally, the method further comprises:
determining the second correction value dn based on 12 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
Optionally, the method further comprises:
determining the third correction value dn based on 13 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is sinusoidal sampling frequency, alpha is stretching coefficient, dn 2 For the center difference information, arcsin () is an arcsine function.
Optionally, the correcting operation for the sinusoidal sampling data set according to the correction reference value and the center column information of the image in the sinusoidal sampling data point set includes:
performing approximate rounding operation according to the correction reference value and the sum value of the central column information of the images in the sinusoidal sampling data point set so as to obtain a correction target value;
and performing correction operation on the sinusoidal sampling data set based on the correction target value.
In a second aspect, the present application also proposes a multi-reference confocal endoscopic image correction apparatus including:
the acquisition unit is used for acquiring the uniform scanning sampling point data set and the sinusoidal sampling data point set;
a first calculation unit for calculating the center difference information of the data in the uniform scanning sampling point data set and the sinusoidal sampling data point set;
the second calculation unit is used for calculating a reference variable based on the center difference information, the stretching coefficient, the galvanometer scanning frequency and the sine sampling frequency;
a determining unit configured to determine a correction reference value based on the reference variable and the reference variable threshold information;
and the correction unit is used for carrying out correction operation on the sine sampling data set according to the correction reference value and the center column information of the image in the sine sampling data point set.
In a third aspect, an electronic device, comprising: the method for correcting a multi-reference-value confocal endoscopic image according to any one of the first aspect includes the steps of a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute the computer program stored in the memory.
In a fourth aspect, a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of multi-reference confocal endoscopic image correction according to any one of the first aspects.
In summary, the multi-reference-value confocal endoscopic image correction method according to the embodiment of the present application includes: acquiring a uniform scanning sampling point data set and a sinusoidal sampling data point set; calculating the center difference information of the data in the uniform scanning sampling point data set and the sinusoidal sampling data point set; calculating a reference variable based on the center difference information, the stretching coefficient, the galvanometer scanning frequency and the sine sampling frequency; determining a correction reference value based on the reference variable and the reference variable threshold information; and correcting the sinusoidal sampling data set according to the correction reference value and the central column information of the image in the sinusoidal sampling data point set. According to the multi-reference-value confocal endoscope image correction method, two data sets are obtained, a uniform scanning data set is adopted as an ideal reference, the difference of the center positions of the two data sets is calculated compared with the two data sets, center difference information is obtained, and a reference variable describing a scanning distortion mode is calculated by combining the center difference, the stretching coefficient, the vibrating mirror scanning frequency and the sinusoidal sampling frequency. And determining a correction reference value as a correction standard according to the reference variable and a preset threshold range. And correcting the sinusoidal sampling data set by using the correction reference value and the image center column information to unify the image structure, improve the quality and reduce or eliminate the distortion.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the specification. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a schematic flow chart of a method for correcting a multi-reference-value confocal endoscope image according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a spatial position of a resonant mirror during scanning according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of a resonant mirror reciprocating scanning sampling principle provided in an embodiment of the present application;
FIG. 4 is a schematic diagram showing the relationship between angular velocity and spatial position of a resonant mirror during scanning according to an embodiment of the present disclosure;
fig. 5 is a schematic diagram of a correction algorithm according to an embodiment of the present application;
FIG. 6 is a schematic diagram of an uncorrected image according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a corrected image according to an embodiment of the present application;
FIG. 8 is a schematic structural diagram of a multi-reference confocal endoscopic image correction apparatus provided in an embodiment of the present application;
fig. 9 is a schematic structural diagram of a multi-reference-value confocal endoscope image correction electronic device according to an embodiment of the present application.
Detailed Description
The terms "first," "second," "third," "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "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. The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application.
FIG. 2 is a schematic diagram of the spatial position of a resonant mirror during scanning in the related art; FIG. 3 is a schematic diagram of a resonant mirror reciprocation scanning sampling principle in the related art; fig. 4 is a schematic diagram showing the relationship between angular velocity and spatial position during scanning of a resonant mirror in the related art. As shown in fig. 2 to 4, the resonant mirror operates on a principle of reciprocating rotation by a certain angle along a rotation axis, turning when reaching the edge of the scanning range, and rotating in the opposite direction. And the angular velocity during scanning varies sinusoidally with the spatial position.
Confocal endoscopy is typically performed using equally spaced samples. The original image obtained by sampling has the following problems due to the characteristics of reciprocation and sine in the scanning process of the resonant mirror: (1) the scanning directions of two adjacent rows are reversed; (2) Because the scanning speed is high, the scanning starting points of two adjacent lines are difficult to be consistent, so that the two adjacent lines are shifted; (3) The angular velocity of the sinusoidal features during scanning is slow at the edges and fast in the middle, resulting in distortion of the whole with stretching at both ends and compression in the middle.
Inversion, shifting and distortion result in the obtained image not conforming to the actual shape of the object. Such images, if applied clinically, may provide the user with erroneous information, which in turn may lead to the user making erroneous diagnostic results, which is not acceptable. Therefore, the confocal endoscope is used for aligning displacement and correcting distortion so as to eliminate the problems caused by the scanning characteristics of the resonant mirror, provide the user with an image which is correct and has the same shape as the actual shape, and further provide accurate diagnosis information for clinic. In order to solve at least some of the problems described above, the present application proposes a multi-reference-value confocal endoscopic image correction method for performing a correction operation on an image.
Referring to fig. 1, a flow chart of a multi-reference-value confocal endoscope image correction method provided in an embodiment of the present application may specifically include:
s110, acquiring a uniform scanning sampling point data set and a sinusoidal sampling data point set;
illustratively, two types of sampled data are collected, a uniformly scanned sampled point data set and a sinusoidal sampled data point set. The endoscopic image from the ideal without scanning distortion is sampled uniformly, while the sinusoidal sampled data set is the set of sampled data points that are distorted by the vibrating mirror motion during the scanning process for the reasons described above.
S120, calculating center difference information of the data in the uniform scanning sampling point data set and the sinusoidal sampling data point set;
by way of example, comparing a uniformly scanned dataset with a sinusoidally sampled dataset, in particular their central positions, difference information between the central points, i.e. central difference information, is calculated, which differences may reflect systematic deviations that may occur during scanning.
S130, calculating a reference variable based on the central difference information, the stretching coefficient, the galvanometer scanning frequency and the sine sampling frequency;
illustratively, the center difference information and the stretch coefficient, the galvanometer scanning frequency, and the sinusoidal sampling frequency are utilized to calculate one or more reference variables. The reference variables are used to describe the scan distortion patterns in the dataset, providing the necessary information for subsequent corrective actions.
S140, determining a correction reference value based on the reference variable and the reference variable threshold information;
illustratively, the corrected reference value is determined based on the calculated reference variable and the preset threshold information. This reference value will be used as a basis for the correction operation, and variable threshold information is used for determining different threshold ranges, corresponding to different correction reference values when the reference variable falls within the different threshold ranges.
And S150, correcting the sinusoidal sampling data set according to the correction reference value and the central column information of the image in the sinusoidal sampling data point set.
Illustratively, correction reference values are used to adjust for distortions in the image in the sinusoidal set of sampled data points caused by galvanometer motion during scanning, manifested as distortions or non-uniform stretching of the image. The center column information of the image provides an important reference point because the distortion is generally symmetrical with respect to the center of the image. Based on this center column information, the image can be appropriately adjusted so that the correction operation affects the entire image uniformly. The correction reference value is determined based on preset threshold information, which aids in determining the degree of correction. If the reference variable indicates less distortion, the corrective action may require only slight adjustments; if the distortion is large, a more significant correction is required. Finally, according to the correction reference value, an actual correction operation is performed. The corrected sine sampling data point set has a more uniform structure, so that the image quality is improved, and the distortion influence is eliminated or weakened.
In summary, according to the multi-reference-value confocal endoscope image correction method provided by the embodiment of the application, two data sets are obtained, a uniform scanning data set is adopted as an ideal reference, the difference of the center positions of the two data sets is calculated compared with the two data sets, center difference information is obtained, and a reference variable describing a scanning distortion mode is calculated by combining the center difference, the stretching coefficient, the vibrating mirror scanning frequency and the sinusoidal sampling frequency. And determining a correction reference value as a correction standard according to the reference variable and a preset threshold range. And correcting the sinusoidal sampling data set by using the correction reference value and the image center column information to unify the image structure, improve the quality and reduce or eliminate the distortion.
In some examples, calculating the reference variable based on the center difference information, the stretch coefficient, the galvanometer scanning frequency, and the sinusoidal sampling frequency includes:
the above reference variable x is calculated according to the following formula:
wherein dn 2 Is the central difference information, alpha is the stretch coefficient, f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
Illustratively, the stretch coefficient α represents the actual degree of stretch experienced by the image during scanning, and this value is calculated or measured beforehand. Scanning frequency f of vibrating mirror scan And sinusoidal sampling frequency f sample Is a key parameter for the operation of the scanning device and characterizes the speed ratio of the scanning and sampling operations. Center difference information dn 2 Is the difference information between the center locations of the image data in the uniform scan dataset and the sinusoidal sample dataset.
In some examples, the reference variable threshold information includes first threshold information and second threshold information, the second threshold information is greater than the first threshold information, the correction reference value includes a first correction reference value, a second correction reference value, and a third correction reference value,
the determining a correction reference value based on the reference variable and the reference variable threshold information includes:
determining the first correction reference value as a correction reference value in the case where the reference variable is smaller than the first threshold information; and/or the number of the groups of groups,
determining the second correction reference value as a correction reference value in the case where the reference variable is greater than the second threshold information; and/or the number of the groups of groups,
and determining the third correction reference value as a correction reference value when the reference variable is greater than or equal to the first threshold information and less than or equal to the second threshold information.
For example, the first threshold information may be-1, the second threshold information may be 1, the first correction reference value may be determined as the correction reference value when the reference variable is less than-1, the second correction reference value may be determined as the correction reference value when the reference variable is greater than 1, and the third correction reference value may be determined as the correction reference value when the reference variable is greater than or equal to-1 and less than or equal to 1.
In some examples, the first corrected reference value dn 11 Second correction value dn 12 And a third correction value dn 13 The calculation can be performed by the following method:
determining the first correction reference value dn based on 11 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
Determining the second correction value dn based on 12 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
Determining the third correction value dn based on 13 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is sinusoidal sampling frequency, alpha is stretching coefficient, dn 2 For the center difference information, arcsin () is an arcsine function.
Illustratively, the first corrected reference value is calculated from the galvanometer sweep frequency and the sinusoidal sampling frequency and is an inverse number of each other, and the third corrected reference value is determined from the galvanometer sweep frequency, the sinusoidal sampling frequency, and an arcsine value that is related based on the product of the stretch coefficient, the center difference information, the galvanometer sweep frequency, and the sinusoidal sampling frequency.
In some examples, the performing a correction operation on the sinusoidal sample data set based on the correction reference value and center column information of an image in the sinusoidal sample data set includes:
performing approximate rounding operation according to the correction reference value and the sum value of the central column information of the images in the sinusoidal sampling data point set so as to obtain a correction target value;
and performing correction operation on the sinusoidal sampling data set based on the correction target value.
Illustratively, the sum of the correction reference value and the image center column information that has been calculated is used. The sum is subjected to an approximate rounding operation to obtain a corrected target value. The correction target value is used to adjust the image data in the sinusoidal sample data set. The correction operation may include a translation, rotation, or other transformation of the data points in the image to compensate for distortion or stretching due to the scan.
An object of embodiments of the present application is to correct image distortion so that the image more accurately reflects actual visual information. In this way, each sampling point of the image is adjusted according to the calculated target value, thereby improving the overall image quality and reducing the influence caused by distortion. Such a correction procedure is typically automated, and can improve processing efficiency, ensuring consistency and repeatability of results.
Specifically, the correction module reads the correction parameters from the correction parameter storage module, and outputs the correction parameters to the computerThe incoming data is corrected. Record a certain behavior vals1[ N ] in the input data 1 ]The data after data correction is vals2[ N ] 2 ]. As shown in fig. 5, the algorithm scans the sample point index n uniformly 2 Subscript n to a sine sampling point 1 Correspondingly, uniformly scanning the subscript n of the sampling point 2 The luminance value is equal to the sine sampling point index n 1 Is a luminance value of (a). Fig. 5 is a schematic diagram of a correction algorithm provided in an embodiment of the present application, and fig. 6 is a schematic diagram of an uncorrected image provided in an embodiment of the present application.
This can be achieved by the following correction algorithm 1 and correction algorithm 2:
the pseudo code of correction algorithm 1 is as follows:
input:
vals1[N 1 ]sinusoidal scan signal data
f scan -galvanometer scanning frequency, in Hz;
f sample -sinusoidal sampling frequency, in Hz;
c, a central column;
alpha-stretch coefficient;
and (3) outputting:
vals2[N 2 ]-uniformly scanning signal data
The steps are as follows:
the round function in 12 rows is a near-round function.
The main steps of the correction algorithm 1 include the following:
1. initializing variables:
vals1[ N ]: an array of the initial data set is stored.
f scan : scanning frequency, in Hz.
f sample : sampling frequency in Hz.
C: center column.
Alpha: the stretch coefficient is used for adjusting the correction amplitude.
2. Calculating intermediate variables:
vals2[ N ]: an array of corrected data is stored.
C 2 : calculated as half the length of the vals1 array, for determining the center position of the dataset.
3. Each data point was processed in cycles:
for N from 0 to N-1 (length of dataset), calculate the distortion d for each point n2 。
d n2 Calculated as n minus C 2 ，d n2 To characterize the offset relative to the dataset center.
4. According to d n2 Different correction methods are chosen for the values of (a):
the first threshold information may be-1, the second threshold information is 1, and when the reference variable is smaller than-1, the reference variable is:determining the first correction reference value as a correction reference value in the case where the reference variable is smaller than the first threshold information; determining the second correction reference value as a correction reference value in the case where the reference variable is greater than the second threshold information; and determining the third correction reference value as a correction reference value when the reference variable is greater than or equal to the first threshold information and less than or equal to the second threshold information.
Determining the first correction reference value dn based on 11 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
Determining the second correction value dn based on 12 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
Determining the third correction value dn based on 13 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is sinusoidal sampling frequency, alpha is stretching coefficient, dn 2 For the center difference information, arcsin () is an arcsine function.
5. Calculating and correcting an index:
using round function to pair c+d 1 Rounding the result of (2) to obtain a corrected index n 1 ；
If n 1 And is smaller than 0 or larger than N-1, the value of the first or last element of the array vals1 is set to be the value of the first or last element of the array vals1 respectively, and index out-of-range is prevented.
If d n1 The corresponding value is obtained directly from vals1 within the effective range. The algorithm effectively assigns a corrected value to each data point, thereby generating a new data set vals2 that reflects the corrected sampled data.
The pseudo code of correction algorithm 2 is as follows:
input:
vals1[N 1 ]sinusoidal scan signal data
f scan -galvanometer scanning frequency, in Hz;
f sample -sinusoidal sampling frequency, in Hz;
c, a central column;
alpha-stretch coefficient;
and (3) outputting:
vals2[N 2 ]-uniformly scanning signal data
The steps are as follows:
the main steps of the correction algorithm 2 include the following:
1. initializing variables:
vals1[ N ]: an array of the initial data set is stored.
f scan : scanning frequency, in Hz.
f sample : sampling frequency in Hz.
C: center column.
Alpha: the stretch coefficient is used for adjusting the correction amplitude.
2. Calculating intermediate variables:
vals2[ N ]: an array of corrected data is stored.
C 2 : calculated as half the length of the vals1 array, for determining the center position of the dataset.
3. Each data point was processed in cycles:
for N from 0 to N-1 (length of dataset), calculate the distortion d for each point n2 。
d n2 Calculated as n minus C 2 ，d n2 To characterize the offset relative to the dataset center.
4. According to d n2 Different correction methods are chosen for the values of (a):
the first threshold information may be-1, the second threshold information is 1, and when the reference variable is smaller than-1, the reference variable is:determining the first correction reference value as a correction reference value in the case where the reference variable is smaller than the first threshold information; determining the second correction reference value as a correction reference value in the case where the reference variable is greater than the second threshold information; and determining the third correction reference value as a correction reference value when the reference variable is greater than or equal to the first threshold information and less than or equal to the second threshold information. />
Determining the first correction reference value dn based on 11 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
Determining the second correction value dn based on 12 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
Determining the third correction value dn based on 13 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is sinusoidal sampling frequency, alpha is stretching coefficient, dn 2 For the center difference information, arcsin () is an arcsine function.
5. Calculating and correcting an index:
using round function to pair c+d 1 Rounding the result of (2) to obtain a corrected index n 1 ；
If n 1 And is smaller than 0 or larger than N-1, the value of the first or last element of the array vals1 is set to be the value of the first or last element of the array vals1 respectively, and index out-of-range is prevented.
If n 1 Between 0 and N-1, then the corresponding element of vals2 passes the weighting parameterAndand (5) carrying out weighted interpolation calculation.
Referring to fig. 7, an embodiment of a multi-reference confocal endoscopic image correction apparatus according to an embodiment of the present application may include:
an acquisition unit 21 for acquiring a uniform scanning sampling point data set and a sinusoidal sampling data point set;
a first calculation unit 22 for calculating center difference information of the data in the uniform scanning sampling point data set and the sinusoidal sampling data point set;
a second calculation unit 23 for calculating a reference variable based on the above-described center difference information, the stretch coefficient, the galvanometer scanning frequency, and the sinusoidal sampling frequency;
a determining unit 24 for determining a correction reference value based on the reference variable and the reference variable threshold information;
and a correction unit 25 for performing a correction operation on the sinusoidal sample data set based on the correction reference value and center column information of the image in the sinusoidal sample data set.
As shown in fig. 8, the embodiment of the present application further provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor, where the processor 320 implements any of the steps of the above-mentioned methods for correcting a multi-reference confocal endoscopic image when executing the computer program 311.
Since the electronic device described in this embodiment is a device for implementing a multi-reference confocal endoscope image correction apparatus in this embodiment, based on the method described in this embodiment, those skilled in the art can understand the specific implementation of the electronic device in this embodiment and various modifications thereof, so how to implement the method in this embodiment in this electronic device will not be described in detail herein, and only those devices for implementing the method in this embodiment by those skilled in the art are within the scope of protection intended in this application.
In a specific implementation, the computer program 311 may implement any of the embodiments corresponding to fig. 1 when executed by a processor.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Embodiments also provide a computer program product comprising computer software instructions that, when run on a processing device, cause the processing device to perform the multi-reference confocal endoscopic image correction procedures of the corresponding embodiments
The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). Computer readable storage media can be any available media that can be stored by a computer or data storage devices such as servers, data centers, etc. that contain an integration of one or more available media. Usable media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid State Disks (SSDs)), among others.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A multi-reference confocal endoscopic image correction method, comprising:
acquiring a uniform scanning sampling point data set and a sinusoidal sampling data point set;
calculating center difference information of the data in the uniform scanning sampling point data set and the sinusoidal sampling data point set;
calculating a reference variable based on the center difference information, the stretching coefficient, the galvanometer scanning frequency and the sine sampling frequency;
determining a corrected reference value based on the reference variable and the reference variable threshold information;
and correcting the sinusoidal sampling data set according to the correction reference value and the central column information of the image in the sinusoidal sampling data point set.
2. The multi-reference confocal endoscopic image correction method of claim 1, wherein the calculating a reference variable based on the center difference information, a stretch coefficient, a galvanometer scanning frequency, and a sinusoidal sampling frequency comprises:
calculating the reference variable x according to the formula:
wherein dn 2 Is the central difference information, alpha is the stretch coefficient, f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
3. The multi-reference-value confocal endoscopic image correction method according to claim 1, wherein the reference variable threshold information includes first threshold information and second threshold information, the second threshold information being greater than the first threshold information, the correction reference values include a first correction reference value, a second correction reference value, and a third correction reference value,
the determining a corrected reference value based on the reference variable and the reference variable threshold information includes:
determining the first correction reference value as a correction reference value in the case where the reference variable is smaller than the first threshold information; and/or the number of the groups of groups,
determining the second correction reference value as a correction reference value in the case where the reference variable is greater than the second threshold information; and/or the number of the groups of groups,
the third correction reference value is determined as a correction reference value in the case where the reference variable is greater than or equal to the first threshold information and less than or equal to the second threshold information.
4. The multi-reference confocal endoscopic image correction method of claim 3, further comprising:
determining the first corrected reference value dn based on 11 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
5. The multi-reference confocal endoscopic image correction method of claim 3, further comprising:
determining the second correction value dn based on 12 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is a sinusoidal sampling frequency.
6. The multi-reference confocal endoscopic image correction method of claim 3, further comprising:
determining the third correction value dn based on 13 ：
Wherein f scan For the scanning frequency of the galvanometer, f sample Is sinusoidal sampling frequency, alpha is stretching coefficient, dn 2 For the center difference information, arcsin () is an arcsine function.
7. The multi-reference confocal endoscopic image correction method of claim 1, wherein said performing a correction operation on the sinusoidal sample data set according to the correction reference value and center column information of an image in the sinusoidal sample data point set comprises:
performing approximate rounding operation according to the sum of the correction reference value and the central column information of the images in the sinusoidal sampling data point set to obtain a correction target value;
and performing correction operation on the sinusoidal sampling data set based on the correction target value.
8. A multi-reference confocal endoscopic image correction apparatus comprising:
the acquisition unit is used for acquiring the uniform scanning sampling point data set and the sinusoidal sampling data point set;
a first calculation unit for calculating center difference information of the data in the uniform scanning sampling point data set and the sinusoidal sampling data point set;
the second calculation unit is used for calculating a reference variable based on the center difference information, the stretching coefficient, the galvanometer scanning frequency and the sine sampling frequency;
a determining unit configured to determine a correction reference value based on the reference variable and the reference variable threshold information;
and the correction unit is used for carrying out correction operation on the sinusoidal sampling data set according to the correction reference value and the central column information of the image in the sinusoidal sampling data point set.
9. An electronic device, comprising: memory and processor, characterized in that the processor is adapted to carry out the steps of the method of multi-reference confocal endoscopic image correction according to any one of claims 1-7 when executing a computer program stored in the memory.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements a method of multi-reference confocal endoscopic image correction according to any one of claims 1-7.
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