CN112734668A - Image restoration method and system - Google Patents
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Abstract
The invention provides an image restoration method and system, and belongs to the technical field of artificial intelligence. The image restoration method comprises the following steps: determining a distance function from the pixel point to an initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point to the initial clustering center; determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center; and determining the target offset of the pixel points according to the intermediate membership degree, and restoring the original image through the target offset of the pixel points. The invention can improve the definition and accuracy of the image.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an image restoration method and system.
Background
In an image acquisition scene, an image is often damaged by a slowly-changing gray scale non-uniform field, and the non-uniform field can cause the gray scale of the same part to change, so that the definition and the accuracy of the image are influenced. In the prior art, the image is restored by utilizing the spatial information of the image and an algorithm with the function of correcting the offset field, and the restored image has high-frequency components and is difficult to clearly display.
Disclosure of Invention
The embodiment of the invention mainly aims to provide an image restoration method and an image restoration system, so as to accurately restore an image and improve the definition and accuracy of the image.
In order to achieve the above object, an embodiment of the present invention provides an image restoration method, including:
determining a distance function from the pixel point to an initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point to the initial clustering center;
determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center;
and determining the target offset of the pixel points according to the intermediate membership degree, and restoring the original image through the target offset of the pixel points.
An embodiment of the present invention further provides an image restoration system, including:
the distance function determining unit is used for determining a distance function from the pixel point to the initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point relative to the initial clustering center;
the intermediate membership determining unit is used for determining the intermediate membership of the pixel point relative to the initial clustering center according to a distance function from the pixel point to the initial clustering center;
and the image restoration unit is used for determining the target offset of the pixel points according to the intermediate membership degree and restoring the original image through the target offset of the pixel points.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the image inpainting method when executing the computer program.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the image inpainting method.
The image restoration method and the system of the embodiment of the invention firstly determine the distance function according to the pixel points of the original image, the initial offset of the pixel points, the initial clustering center and the initial membership, then determine the intermediate membership according to the distance function, then determine the target offset of the pixel points according to the intermediate membership, and accurately restore the original image through the target offset of the pixel points, thereby improving the definition and the accuracy of the image.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of an image inpainting method according to an embodiment of the present invention;
FIG. 2 is a flow chart of an image inpainting method in another embodiment of the present invention;
FIG. 3 is a flow chart of determining a target offset in one embodiment of the present invention;
FIG. 4 is a flow chart of determining a target offset in another embodiment of the present invention;
FIG. 5 is a ticket image unaffected by the illumination-nonuniform field;
FIG. 6 is a ticket image affected by a field of illumination inhomogeneities;
FIG. 7 is a ticket image repaired by the image repair method of the present invention;
FIG. 8 is a block diagram showing the configuration of an image repair system according to an embodiment of the present invention;
fig. 9 is a block diagram of a computer device in the embodiment of the present invention.
Detailed Description
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.
As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
In view of the difficulty in clearly displaying images in the prior art, the embodiment of the invention provides an image restoration method, which can accurately restore images and improve the definition and accuracy of the images. The present invention will be described in detail below with reference to the accompanying drawings.
An image that is unevenly illuminated may be equivalent to an image that is not actually contaminated by an uneven field multiplied by an uneven field that varies slowly in the spatial domain:
Zi＝XiGi，i∈{i,2,3,...,N}；
in the formula, ZiFor observing the value of the resulting image at the ith point, XiValue at the i-th point, G, for an image not contaminated by a uniform fieldiThe value of the multiplicative inhomogeneous field at the ith point, and N is the total pixel point of the image. For convenience of calculation, taking logarithm on two sides of the formula to obtain:
zi＝xi+βi，i∈{i,2,3,...,N}；
in the formula, ziLogarithmic value, x, at point i for the observed imageiLogarithmic value, beta, at the i-th point for an image not contaminated by a uniform fieldiFor the logarithmic value of the multiplicative uneven field at the ith point, the aim of the gray scale image correction and restoration is to correct the value from ziIn (1) xiRecoveryAnd (6) discharging.
For example, when a bank collects images such as counter certificate information, invoice contract information, bill real objects and the like, the collected images are not clearly influenced by factors such as uneven illumination, light reflection and the like.
FIG. 1 is a flowchart illustrating an image restoration method according to an embodiment of the present invention. FIG. 2 is a flowchart of an image restoration method according to another embodiment of the present invention. As shown in fig. 1 and 2, the image restoration method includes:
s101: and determining a distance function from the pixel point to the initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point to the initial clustering center.
The initial offset of the pixel point may be a smaller value, for example, 0.01.
In one embodiment, before performing S101, the method further includes:
acquiring an initial image; and removing the background area of the initial image to obtain an original image.
In specific implementation, a morphological method can be used to remove the background region and the region not of interest of the initial image, so as to obtain the original image.
In one embodiment, a finite set of Y ═ Y is assumed1,y2,y3,……,yNIs a set of N pixels, c is a predetermined number of classes, yiIs the ith pixel point, vjIs the jth cluster center, uj(yi) If the membership degree of the ith pixel point relative to the jth clustering center is adopted, the clustering criterion function is defined by the membership function as follows:
wherein J (U, V) is a clustering criterion function, | | yi-vj| is yiTo vj(j＝1,2,3,...,c)Square of the euclidean distance of (d); b is fuzzy weighted power exponent which is a parameter capable of controlling the fuzzy degree of the clustering result; u is the fuzzification submatrix of Y while satisfying Uj(yi)∈[0,1]And
v is the set of cluster centers for Y.
The present invention improves the FCM algorithm and incorporates spatial relationship constraints: the objective of the FCM clustering algorithm is to obtain V and U that minimize the criteria function. The algorithm defines each pixel yiAbout the cluster center vjDegree of membership u ofj(yi) Comprises the following steps:
wherein v ismIs the mth cluster center.
According to
And
the following formula can be obtained to determine the distance function from the pixel point to the initial clustering center:
wherein d is2(yi,vj) Is the ith pixel point yiTo the jth initial clustering center vjDistance function of yiIs the ith pixel point, betaiIs the initial offset, v, of the ith pixel pointjFor the jth initial cluster center, λ is the first regularized spatial constraint relationship coefficient,λ∈[0,1]xi is a second adjustment space constraint relation coefficient, and xi belongs to [0,1 ]]，uj(yk) K field point y corresponding to i pixel pointkInitial degree of membership, G, for the jth initial cluster centerikIs the absolute value of the gray difference between the ith pixel point and the corresponding kth field point, PikIs the block distance between the ith pixel point and the corresponding kth domain point, and s is yiIs a central point, and yiCorresponding domain point ykThe number of (2); d2(yi,vm) Is the ith pixel point yiTo the m-th initial clustering center vmV is a distance function ofmFor the m-th initial cluster center, um(yk) K field point y corresponding to i pixel pointkInitial membership for the mth initial cluster center.
Therefore, the clustering ambiguity matrix is not only determined by the gray level of the pixel, but also influenced by the pixel points in other fields.
S102: and determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center.
In one embodiment, the intermediate degree of membership may be determined by the following formula:
wherein u isj(yi) Is the ith pixel point yiWith respect to the intermediate degree of membership of the jth initial cluster center, b is a fuzzy weighted power exponent.
S103: and determining the target offset of the pixel points according to the intermediate membership degree, and restoring the original image through the target offset of the pixel points.
In specific implementation, the corresponding pixel points of the original image can be restored according to the target offset (uneven field) of the pixel points to remove the uneven field of illumination and restore the original image.
FIG. 3 is a flow chart of determining a target offset in an embodiment of the present invention. As shown in fig. 3, determining the target offset of the pixel point according to the intermediate membership degree includes:
s201: and determining the intermediate offset of the pixel point according to the intermediate membership and the initial clustering center.
In one embodiment, the intermediate offset of the pixel point can be determined by the following formula:
wherein is beta'iThe intermediate offset of the ith pixel point.
S202: and determining the target offset of the pixel point according to the intermediate offset.
Fig. 4 is a flow chart of determining a target offset in another embodiment of the present invention. As shown in fig. 4, S202 further includes:
s301: and determining a middle clustering center according to the middle offset and the middle membership.
In one embodiment, the intermediate cluster center may be determined by the following formula:
wherein, v'jFor the jth intermediate cluster center, (u (y)i))maxIs the ith pixel point yiMaximum value of intermediate degree of membership with respect to the initial cluster center.
S302: and determining the target offset of the pixel point through the intermediate membership degree, the intermediate offset and the intermediate clustering center.
In one embodiment, S302 further includes:
1. and respectively replacing the initial membership degree, the initial offset and the initial clustering center by the intermediate membership degree, the intermediate offset and the intermediate clustering center, and then carrying out corresponding iterative computation.
The domain point corresponding to the pixel point is also one of the pixel points of the original image, so that the initial membership degree of the domain point corresponding to the pixel point relative to the initial clustering center can be replaced according to the intermediate membership degree of the pixel point relative to the initial clustering center.
2. And when the current iteration times are equal to the preset iteration times, taking the intermediate offset obtained by current iteration calculation as the target offset of the pixel point.
The execution subject of the image restoration method shown in fig. 1 may be a computer. As can be seen from the flow shown in fig. 1, the image restoration method according to the embodiment of the present invention determines a distance function according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center, and the initial membership, determines the intermediate membership according to the distance function, determines the target offset of the pixel point according to the intermediate membership, and accurately restores the original image according to the target offset of the pixel point, so that the definition and the accuracy of the image can be improved.
The specific process of the embodiment of the invention is as follows:
1. and acquiring an initial image, and removing a background area and an uninteresting area of the initial image to obtain an original image.
2. And determining a distance function from the pixel point to the initial clustering center according to the pixel point, the initial offset of the pixel point, the initial clustering center and the initial membership of the field point corresponding to the pixel point to the initial clustering center.
3. And determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center.
4. And determining the intermediate offset of the pixel point according to the intermediate membership and the initial clustering center.
5. And determining a middle clustering center according to the middle offset and the middle membership.
6. And judging whether the current iteration times are equal to the preset iteration times or not.
7. And when the current iteration times are equal to the preset iteration times, taking the intermediate offset obtained by current iteration calculation as the target offset of the pixel point, otherwise, replacing the initial membership according to the intermediate membership, replacing the initial offset according to the intermediate offset, replacing the initial clustering center according to the intermediate clustering center, and returning to the step 2.
8. And restoring the original image according to the target offset of the pixel point.
FIG. 5 is an image of a bill unaffected by a field of illumination inhomogeneities. FIG. 6 is an image of a document subject to a field of illumination non-uniformity. Fig. 7 is a bill image restored by the image restoration method of the present invention. As shown in fig. 5-7, the repaired bill image can be recognized by human eyes, so that the bill image can be filed and stored as image information, and the purpose of image repair is achieved.
In summary, the invention provides a method for repairing an image with uneven illumination, which not only introduces an offset field function, but also introduces the spatial domain information of the image into an FCM clustering criterion function based on gray information, so that the original image can be accurately repaired, and the definition and the accuracy of the image are improved.
Based on the same inventive concept, the embodiment of the invention also provides an image restoration system, and as the principle of solving the problems of the system is similar to the image restoration method, the implementation of the system can refer to the implementation of the method, and repeated parts are not described again.
Fig. 8 is a block diagram of the image restoration system in the embodiment of the present invention. As shown in fig. 8, the image restoration system includes:
the distance function determining unit is used for determining a distance function from the pixel point to the initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point relative to the initial clustering center;
the intermediate membership determining unit is used for determining the intermediate membership of the pixel point relative to the initial clustering center according to a distance function from the pixel point to the initial clustering center;
and the image restoration unit is used for determining the target offset of the pixel points according to the intermediate membership degree and restoring the original image through the target offset of the pixel points.
In one embodiment, the image inpainting unit is specifically configured to:
determining the intermediate offset of the pixel point according to the intermediate membership and the initial clustering center;
and determining the target offset of the pixel point according to the intermediate offset.
In one embodiment, the image restoration unit is further configured to:
determining a middle clustering center according to the middle offset and the middle membership;
and determining the target offset of the pixel point through the intermediate membership degree, the intermediate offset and the intermediate clustering center.
In one embodiment, the image restoration unit is further configured to:
respectively replacing the initial membership degree, the initial offset and the initial clustering center by the intermediate membership degree, the intermediate offset and the intermediate clustering center, and then carrying out corresponding iterative computation;
and when the current iteration times are equal to the preset iteration times, taking the intermediate offset obtained by current iteration calculation as the target offset of the pixel point.
To sum up, the image restoration system according to the embodiment of the present invention determines a distance function according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center, and the initial membership of the domain point corresponding to the pixel point with respect to the initial clustering center, determines an intermediate membership according to the distance function, determines the target offset of the pixel point according to the intermediate membership, and accurately restores the original image according to the target offset of the pixel point, so that the definition and the accuracy of the image can be improved.
The embodiment of the present invention further provides a specific implementation manner of a computer device, which is capable of implementing all steps in the image restoration method in the foregoing embodiment. Fig. 9 is a block diagram of a computer device in an embodiment of the present invention, and referring to fig. 9, the computer device specifically includes the following:
a processor (processor)901 and a memory (memory) 902.
The processor 901 is configured to call a computer program in the memory 902, and the processor implements all the steps in the image repairing method in the above embodiments when executing the computer program, for example, the processor implements the following steps when executing the computer program:
determining a distance function from the pixel point to an initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point to the initial clustering center;
determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center;
and determining the target offset of the pixel points according to the intermediate membership degree, and restoring the original image through the target offset of the pixel points.
To sum up, the computer device of the embodiment of the present invention determines a distance function according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center, and the initial membership of the domain point corresponding to the pixel point with respect to the initial clustering center, determines an intermediate membership according to the distance function, determines the target offset of the pixel point according to the intermediate membership, and precisely restores the original image according to the target offset of the pixel point, so that the definition and accuracy of the image can be improved.
An embodiment of the present invention further provides a computer-readable storage medium capable of implementing all the steps in the image restoration method in the foregoing embodiment, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, implements all the steps in the image restoration method in the foregoing embodiment, for example, when the processor executes the computer program, the processor implements the following steps:
determining a distance function from the pixel point to an initial clustering center according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center and the initial membership degree of the field point corresponding to the pixel point to the initial clustering center;
determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center;
and determining the target offset of the pixel points according to the intermediate membership degree, and restoring the original image through the target offset of the pixel points.
To sum up, the computer-readable storage medium according to the embodiment of the present invention determines a distance function according to the pixel point of the original image, the initial offset of the pixel point, the initial clustering center, and the initial membership of the field point corresponding to the pixel point with respect to the initial clustering center, determines an intermediate membership according to the distance function, determines the target offset of the pixel point according to the intermediate membership, and precisely restores the original image according to the target offset of the pixel point, so that the definition and accuracy of the image can be improved.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Those of skill in the art will further appreciate that the various illustrative logical blocks, units, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.
The various illustrative logical blocks, or elements, or devices described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be located in a user terminal. In the alternative, the processor and the storage medium may reside in different components in a user terminal.
In one or more exemplary designs, the functions described above in connection with the embodiments of the invention may be implemented in hardware, software, firmware, or any combination of the three. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media that facilitate transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, such computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store program code in the form of instructions or data structures and which can be read by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Additionally, any connection is properly termed a computer-readable medium, and, thus, is included if the software is transmitted from a website, server, or other remote source via a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wirelessly, e.g., infrared, radio, and microwave. Such discs (disk) and disks (disc) include compact disks, laser disks, optical disks, DVDs, floppy disks and blu-ray disks where disks usually reproduce data magnetically, while disks usually reproduce data optically with lasers. Combinations of the above may also be included in the computer-readable medium.
Claims (10)
1. An image restoration method, comprising:
determining a distance function from a pixel point to an initial clustering center according to the pixel point of an original image, the initial offset of the pixel point, and the initial membership of the initial clustering center and a field point corresponding to the pixel point to the initial clustering center;
determining the intermediate membership degree of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center;
and determining the target offset of the pixel points according to the intermediate membership degree, and restoring the original image through the target offset of the pixel points.
2. The image inpainting method of claim 1, wherein determining the target offset of a pixel point according to the intermediate membership comprises:
determining the intermediate offset of the pixel point according to the intermediate membership and the initial clustering center;
and determining the target offset of the pixel point according to the intermediate offset.
3. The image inpainting method of claim 2, wherein determining the target offset of the pixel point according to the intermediate offset further comprises:
determining a middle clustering center according to the middle offset and the middle membership degree;
and determining the target offset of the pixel point according to the intermediate membership degree, the intermediate offset and the intermediate clustering center.
4. The image inpainting method of claim 3, wherein determining the target offset for a pixel point from the intermediate degree of membership, the intermediate offset, and the intermediate cluster center further comprises:
respectively replacing the initial membership degree, the initial offset and the initial clustering center by the intermediate membership degree, the intermediate offset and the intermediate clustering center, and then carrying out corresponding iterative computation;
and when the current iteration times are equal to the preset iteration times, taking the intermediate offset obtained by current iteration calculation as the target offset of the pixel point.
5. An image inpainting system, comprising:
the distance function determining unit is used for determining a distance function from the pixel point to the initial clustering center according to the pixel point of the original image, the initial offset of the pixel point and the initial membership degree of the initial clustering center and the field point corresponding to the pixel point;
the intermediate membership determining unit is used for determining the intermediate membership of the pixel point relative to the initial clustering center according to the distance function from the pixel point to the initial clustering center;
and the image restoration unit is used for determining the target offset of the pixel points according to the intermediate membership degree and restoring the original image according to the target offset of the pixel points.
6. The image inpainting system of claim 5, wherein the image inpainting unit is specifically configured to:
determining the intermediate offset of the pixel point according to the intermediate membership and the initial clustering center;
and determining the target offset of the pixel point according to the intermediate offset.
7. The image inpainting system of claim 6, wherein the image inpainting unit is further to:
determining a middle clustering center according to the middle offset and the middle membership degree;
and determining the target offset of the pixel point according to the intermediate membership degree, the intermediate offset and the intermediate clustering center.
8. The image inpainting system of claim 7, wherein the image inpainting unit is further to:
respectively replacing the initial membership degree, the initial offset and the initial clustering center by the intermediate membership degree, the intermediate offset and the intermediate clustering center, and then carrying out corresponding iterative computation;
and when the current iteration times are equal to the preset iteration times, taking the intermediate offset obtained by current iteration calculation as the target offset of the pixel point.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, characterized in that the processor implements the steps of the image inpainting method according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the image inpainting method according to any one of claims 1 to 4.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106327432A (en) * | 2015-06-18 | 2017-01-11 | 北京大学 | Image restoration method and device based on offset quantity |
| CN106600610A (en) * | 2016-12-07 | 2017-04-26 | 上海智臻智能网络科技股份有限公司 | FCM image segmentation method and apparatus |
| CN109285175A (en) * | 2018-08-15 | 2019-01-29 | 中国科学院苏州生物医学工程技术研究所 | The fuzzy clustering image partition method filtered based on morphological reconstruction and degree of membership |
-
2021
- 2021-01-07 CN CN202110016954.1A patent/CN112734668A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106327432A (en) * | 2015-06-18 | 2017-01-11 | 北京大学 | Image restoration method and device based on offset quantity |
| CN106600610A (en) * | 2016-12-07 | 2017-04-26 | 上海智臻智能网络科技股份有限公司 | FCM image segmentation method and apparatus |
| CN109285175A (en) * | 2018-08-15 | 2019-01-29 | 中国科学院苏州生物医学工程技术研究所 | The fuzzy clustering image partition method filtered based on morphological reconstruction and degree of membership |
Non-Patent Citations (1)
| Title |
|---|
| 吴欢;李传富;冯焕清;刘军伟: "基于模糊均值聚类算法的灰度不均匀脑MR图像的分割", 北京生物医学工程, vol. 27, no. 03, 30 June 2008 (2008-06-30), pages 1 - 3 * |
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