CN110047083B - Image noise point identification method, server and storage medium - Google Patents
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Abstract
The invention discloses an image noise point identification method, which is applied to a server and comprises the steps of obtaining an image to be processed and preprocessing the image; extracting a binary boundary image of the preprocessed image according to a predetermined image edge detection algorithm, performing OTSU thresholding on the preprocessed image to obtain a binary region image, and performing line distance transformation on the binary region image to obtain at least one image block; carrying out connected region analysis on the image block to obtain a rectangular frame surrounding the image block so as to mark the position of the image block; acquiring preset information of the image block, and calculating a first preset variable value R1 and a second preset variable value R2 of the image block according to a calculation rule; and if the preset variable value meets the first threshold condition, judging the image block to be a noise area and displaying the image block in a first preset color, and if the preset variable value meets the second threshold condition, judging the image block to be a normal area and displaying the image block in a second preset color. The method can effectively distinguish the noise region from the normal text region in the image, and mark the noise region and the normal text region with different colors.
Description
Technical Field
The present invention relates to the field of image recognition technologies, and in particular, to an image noise point recognition method, a server, and a storage medium.
Background
Image noise (noise) refers to unnecessary or redundant interference information present in image data, and the presence of noise seriously affects the quality of a document image, so that a noise region needs to be distinguished from a normal text region before image enhancement processing and classification processing.
The noise detection method in the prior art mainly comprises the step of comparing the depth values of pixel points in specific areas of a plurality of images, and is suitable for the condition that the pixel distribution difference between a noise area and a visible component area is obvious.
However, the pixel distribution difference between the noise region and the visible component region in most images is not obvious, the noise region and some visible component regions are small in area and similar in shape, and if the noise region detection method in the prior art is still adopted, not only can noise be detected or is difficult to detect, but also effective components can be marked as noise.
Disclosure of Invention
The invention mainly aims to provide an image noise point identification method, a server and a storage medium, and aims to solve the technical problems that noise points are difficult to detect and even normal texts can be marked as noise points in the prior art.
In order to achieve the above object, the present invention provides an image noise point identification method, applied to a server, the method including:
an acquisition step: acquiring an image to be processed, and preprocessing the image;
the processing steps are as follows: extracting a binary boundary image of the preprocessed image according to a predetermined image edge detection algorithm, performing OTSU thresholding on the preprocessed image to obtain a binary region image, and performing line distance transformation on the binary region image to obtain at least one image block;
a marking step: analyzing a connected region of the image block to obtain a rectangular frame surrounding the image block so as to mark the position of the image block;
a calculation step: acquiring preset information of the image block, and calculating a first preset variable value R1 and a second preset variable value R2 of the image block according to a predetermined calculation rule; and
a judging step: and if the preset variable value meets a first threshold condition, judging the image block to be a noise area and displaying the image block in a first preset color, and if the preset variable value meets a second threshold condition, judging the image block to be a normal area and displaying the image block in a second preset color.
Preferably, the image edge detection algorithm is a Canny algorithm, and the region extraction is an OTSU thresholding algorithm.
Preferably, the preset information includes a height value and a width value of the rectangular frame surrounding the image block and a total number of white pixels in the image block.
Preferably, the predetermined calculation rule is:
the first preset variable value R1= the area of the bright point in the block, and the second preset variable value R2= the area of the boundary in the block, wherein the area of the bright point in the block is the total number of the pixels in the binary area in the image block, and the area of the boundary in the block is the total number of the canny boundary pixels in the image block.
To achieve the above object, the present invention further provides a server, including a memory and a processor, where the memory stores an image noise point identification program, and the image noise point identification program, when executed by the processor, implements the following steps:
an acquisition step: acquiring an image to be processed, and preprocessing the image;
the processing steps are as follows: extracting a binary boundary image of the preprocessed image according to a predetermined image edge detection algorithm, performing OTSU thresholding on the preprocessed image to obtain a binary region image, and performing line distance transformation on the binary region image to obtain at least one image block;
a marking step: analyzing a connected region of the image block to obtain a rectangular frame surrounding the image block so as to mark the position of the image block;
a calculation step: acquiring preset information of the image block, and calculating a first preset variable value R1 and a second preset variable value R2 of the image block according to a predetermined calculation rule; and
a judging step: and if the preset variable value meets a first threshold condition, judging the image block to be a noise area and displaying the image block in a first preset color, and if the preset variable value meets a second threshold condition, judging the image block to be a normal area and displaying the image block in a second preset color.
Preferably, the image edge detection algorithm is a Canny algorithm, and the region extraction is an OTSU thresholding algorithm.
Preferably, the preset information includes a height value and a width value of the rectangular frame surrounding the image block and a total number of white pixels in the image block.
Preferably, the predetermined calculation rule is:
the first preset variable value R1= the area of the bright point in the block, and the second preset variable value R2= the area of the boundary in the block, wherein the area of the bright point in the block is the total number of the pixels in the binary area in the image block, and the area of the boundary in the block is the total number of the canny boundary pixels in the image block.
To achieve the above object, the present invention further provides a computer readable storage medium having stored thereon an image noise point identification program, which is executable by one or more processors to implement the steps of the image noise point identification method as described above.
The image noise point identification method, the server and the storage medium provided by the invention have the advantages that the image to be processed is preprocessed, the binary boundary image of the preprocessed image is extracted according to the predetermined image edge detection algorithm, the preprocessed image is subjected to OTSU thresholding to obtain a binary region image, and the binary region image is subjected to line distance transformation to obtain at least one image block; carrying out connected region analysis on the image block to obtain a rectangular frame surrounding the image block so as to mark the position of the image block; acquiring preset information of the image block, and calculating a first preset variable value R1 and a second preset variable value R2 of the image block according to a calculation rule; and if the preset variable value meets the first threshold condition, judging the image block to be a noise area and displaying the image block in a first preset color, and if the preset variable value meets the second threshold condition, judging the image block to be a normal area and displaying the image block in a second preset color. The method can effectively distinguish the noise region from the normal text region in the image, and mark the noise region and the normal text region with different colors.
Drawings
FIG. 1 is a diagram of an application environment of a server according to a preferred embodiment of the present invention;
FIG. 2 is a block diagram of a preferred embodiment of the image noise identification process of FIG. 1;
FIG. 3 is a flowchart illustrating a method for identifying noise in an image according to a preferred embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. 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.
It should be noted that the description relating to "first", "second", etc. in the present invention is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
The invention provides a server 1.
The server 1 may be one or more of a rack server, a blade server, a tower server, or a rack server. The server 1 includes, but is not limited to, a memory 11, a processor 12, and a network interface 13.
The memory 11 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 11 may in some embodiments be an internal storage unit of the server 1, for example a hard disk of the server 1. The memory 11 may also be an external storage device of the server 1 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the server 1.
Further, the memory 11 may also include both an internal storage unit of the server 1 and an external storage device. The memory 11 may be used not only to store application software installed in the server 1 and various types of data such as a code of the image noise recognition program 10, but also to temporarily store data that has been output or is to be output.
The network interface 13 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), typically used to establish a communication link between the server and other electronic devices.
The network may be the internet, a cloud network, a wireless fidelity (Wi-Fi) network, a Personal Area Network (PAN), a Local Area Network (LAN), and/or a Metropolitan Area Network (MAN). Various devices in the network environment may be configured to connect to the communication network according to various wired and wireless communication protocols. Examples of such wired and wireless communication protocols may include, but are not limited to, at least one of: transmission control protocol and internet protocol (TCP/IP), client datagram protocol (UDP), hypertext transfer protocol (HTTP), File Transfer Protocol (FTP), ZigBee, EDGE, IEEE 802.11, optical fidelity (Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g, multi-hop communications, wireless Access Points (APs), device-to-device communications, cellular communication protocol, and/or BlueTooth (BlueTooth) communication protocol, or a combination thereof.
Optionally, the server 1 may further include a client interface, the client interface may include a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional client interface may further include a standard wired interface and a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is used for displaying information processed in the server 1 and for displaying a visual client interface.
While FIG. 1 shows only a server 1 having components 11-15 and an image noise identification program 10, those skilled in the art will appreciate that the configuration shown in FIG. 1 is not intended to be limiting of server 1, and may include fewer or more components than shown, or some components in combination, or a different arrangement of components.
In this embodiment, the image noise recognition program 10 of fig. 1, when executed by the processor 12, implements the following steps:
an acquisition step: acquiring an image to be processed, and preprocessing the image;
the processing steps are as follows: extracting a binary boundary image of the preprocessed image according to a predetermined image edge detection algorithm, performing OTSU thresholding on the preprocessed image to obtain a binary region image, and performing line distance transformation on the binary region image to obtain at least one image block; a marking step: analyzing a connected region of the image block to obtain a rectangular frame surrounding the image block so as to mark the position of the image block;
a calculation step: acquiring preset information of the image block, and calculating a first preset variable value R1 and a second preset variable value R2 of the image block according to a predetermined calculation rule; and
a judging step: and if the preset variable value meets a first threshold condition, judging the image block to be a noise area and displaying the image block in a first preset color, and if the preset variable value meets a second threshold condition, judging the image block to be a normal area and displaying the image block in a second preset color.
For a detailed description of the above steps, please refer to the following description of fig. 2 regarding a schematic diagram of program modules of an embodiment of the image noise point identification program 10 and fig. 3 regarding a schematic diagram of a method flow of an embodiment of the image noise point identification method.
Referring to FIG. 2, a block diagram of an embodiment of the image noise identification procedure 10 of FIG. 1 is shown. The image noise point identifying program 10 is divided into a plurality of modules, which are stored in the memory 11 and executed by the processor 12 to complete the present invention. The modules referred to herein are referred to as a series of computer program instruction segments capable of performing specified functions.
In this embodiment, the image noise point identification program 10 includes an obtaining module 110, a processing module 120, a marking module 130, a calculating module 0, and a determining module 150.
The obtaining module 110 is configured to obtain an image to be processed, and perform preprocessing on the image.
In this embodiment, the image to be processed is preprocessed to avoid the influence of the background on the image, and in this embodiment, a 3 × 3 median filter is first used to remove the point noise in the input original image, and the independent noise points (salt and pepper noise) with the size of 1-2 pixels in the image are filtered.
The processing module 120 is configured to extract a binary boundary image of the preprocessed image according to a predetermined image edge detection algorithm, perform OTSU (law of large law) thresholding on the preprocessed image to obtain a binary region image, and perform line distance transformation on the binary region image to obtain at least one image block.
In this embodiment, the boundary image of the preprocessed image is extracted by a predetermined image edge detection algorithm, where the image edge detection refers to obtaining edge information of an object by using the discontinuity characteristic of the image, and currently popular edge detection methods include a Sobel algorithm, a Laplace algorithm, a Prewitt algorithm, a wavelet transform method, and the like. The Canny algorithm starts from three criteria of good edge detection performance, good positioning performance and single edge response, and can be better used for edge detection by utilizing an optimized numerical solution method. The method comprises four steps of image smoothing, gradient amplitude calculation, gradient image non-maximum value inhibition, high-low double-threshold value processing and the like. Compared with Prewitt algorithm, Sobel algorithm and the like, the Canny algorithm has the advantages of effectively filtering noise influence and retaining detail information of the image in the aspect of image edge detection, and therefore the Canny algorithm is adopted in the image edge detection algorithm in the scheme.
In this embodiment, the OTSU algorithm is also called a maximum inter-class difference method, sometimes called an atrazine algorithm, which was proposed by atrazine in 1979, is considered as an optimal algorithm for selecting a threshold in image segmentation, is simple in calculation, and is not affected by image brightness and contrast, so that the OTSU algorithm is widely applied to digital image processing. The image is divided into a background part and a foreground part according to the gray characteristic of the image. Since the variance is a measure of the uniformity of the gray distribution, the larger the inter-class variance between the background and the foreground is, the larger the difference between the two parts constituting the image is, and the smaller the difference between the two parts is when part of the foreground is mistaken for the background or part of the background is mistaken for the foreground. Thus, a segmentation that maximizes the inter-class variance means that the probability of false positives is minimized.
The marking module 130 is configured to perform connected region analysis on the image block to obtain a rectangular frame surrounding the image block, so as to mark a position of the image block.
In this embodiment, a rectangular frame surrounding an image block is obtained by performing connected area analysis on the image block to mark the position of the image block. The purpose of visually identifying the text area in the image is achieved.
The connected region analysis is a connected region marker, and is a common and basic method in a plurality of application fields such as image analysis processing. For example: character segmentation extraction in OCR (license plate recognition, text recognition, subtitle recognition, and the like), moving foreground object segmentation and extraction in visual tracking (pedestrian intrusion detection, legacy object detection, vision-based vehicle detection and tracking, and the like), medical image processing (object-of-interest region extraction), and the like. That is to say, the connected component analysis method can be used in an application scene in which a foreground object needs to be extracted for subsequent processing, and usually, the object of connected component analysis processing is a binarized image, which is the basis of all binary image analysis. The image to be retrieved needs to be converted into a binary image first. Connected Component (Connected Component) generally refers to an image area (Blob) composed of foreground pixels having the same pixel value and adjacent positions in an image. Connected Component Analysis (Connected Component Analysis) refers to finding and labeling each Connected region in an image.
There are many kinds of connected region labeling algorithms, which may be implemented by an algorithm used in a connected region labeling function bwleal in matlab, or by a labeling algorithm used in an open source library cvBlob, and this is not specifically limited in the embodiment of the present invention.
The calculating module 140 is configured to obtain preset information of the image block, and calculate a first preset variable value R1 and a second preset variable value R2 of the image block according to a predetermined calculation rule.
In this embodiment, the type (noise region or normal region) of the image block may be distinguished by obtaining preset information of the image block (the preset information includes a height value and a width value of a rectangular frame surrounding the image block, and a total number of white pixels in the image block), calculating a first preset variable value R1 and a second preset variable value R2 of the image block according to a predetermined calculation rule, and performing a judgment analysis on the calculated first preset variable value R1 and second preset variable value R2 through the judgment module 150.
The predetermined calculation rule is: the first preset variable value R1= the area of the bright spot in the block, and the second preset variable value R2= the area of the boundary in the block
The area of bright spots in the image block is the total number of binary region pixels in the image block, and the area of boundary in the image block is the total number of canny boundary pixels in the image block.
The determining module 150 is configured to determine the type of the image block, i.e., the noise region or the normal region.
In the present embodiment, the first preset variable value R1 and the second preset variable value R2 satisfy the first threshold condition (e.g., R2) by analyzing the first preset variable value R1 and the second preset variable value R2 for consistency1<40 and height H of the block<10) Judging the image block as a small noise area, displaying the image block in a first predetermined color (e.g., red), and if the predetermined variable value satisfies a second threshold condition (e.g., a second predetermined variable value R2/R)1<0.5 or R1Area per block>0.6), the image block is judged to be a large block noise and displayed in a second preset color (e.g., yellow). Therefore, the noise region and the normal region in the image can be visually distinguished.
In addition, the invention also provides an image noise point identification method. Fig. 3 is a schematic method flow chart of the method for identifying image noise according to the embodiment of the present invention. When the processor 12 of the server 1 executes the image noise recognition program 10 stored in the memory 11, the following steps of the image noise recognition method are implemented:
s110, acquiring an image to be processed, and preprocessing the image.
In this embodiment, the image to be processed is preprocessed to avoid the influence of the background on the image, and in this embodiment, a 3 × 3 median filter is first used to remove the point noise in the input original image, and the independent noise points (salt and pepper noise) with the size of 1-2 pixels in the image are filtered.
And S120, extracting a binary boundary image of the preprocessed image according to a predetermined image edge detection algorithm, performing OTSU (Hypertext transfer) thresholding on the preprocessed image to obtain a binary region image, and performing line distance conversion on the binary region image to obtain at least one image block.
In this embodiment, the boundary image of the preprocessed image is extracted by a predetermined image edge detection algorithm, where the image edge detection refers to obtaining edge information of an object by using the discontinuity characteristic of the image, and currently popular edge detection methods include a Sobel algorithm, a Laplace algorithm, a Prewitt algorithm, a wavelet transform method, and the like. The Canny algorithm starts from three criteria of good edge detection performance, good positioning performance and single edge response, and can be better used for edge detection by utilizing an optimized numerical solution method. The method comprises four steps of image smoothing, gradient amplitude calculation, gradient image non-maximum value inhibition, high-low double-threshold value processing and the like. Compared with Prewitt algorithm, Sobel algorithm and the like, the Canny algorithm has the advantages of effectively filtering noise influence and retaining detail information of the image in the aspect of image edge detection, and therefore the Canny algorithm is adopted in the image edge detection algorithm in the scheme.
In this embodiment, the OTSU algorithm is also called a maximum inter-class difference method, sometimes called an atrazine algorithm, which was proposed by atrazine in 1979, is considered as an optimal algorithm for selecting a threshold in image segmentation, is simple in calculation, and is not affected by image brightness and contrast, so that the OTSU algorithm is widely applied to digital image processing. The image is divided into a background part and a foreground part according to the gray characteristic of the image. Since the variance is a measure of the uniformity of the gray distribution, the larger the inter-class variance between the background and the foreground is, the larger the difference between the two parts constituting the image is, and the smaller the difference between the two parts is when part of the foreground is mistaken for the background or part of the background is mistaken for the foreground. Thus, a segmentation that maximizes the inter-class variance means that the probability of false positives is minimized.
S130, conducting connected region analysis on the image blocks to obtain a rectangular frame surrounding the image blocks so as to mark the positions of the image blocks.
In this embodiment, a rectangular frame surrounding an image block is obtained by performing connected area analysis on the image block to mark the position of the image block. The purpose of visually identifying the text area in the image is achieved.
The connected region analysis is a connected region marker, and is a common and basic method in a plurality of application fields such as image analysis processing. For example: character segmentation extraction in OCR (license plate recognition, text recognition, subtitle recognition, and the like), moving foreground object segmentation and extraction in visual tracking (pedestrian intrusion detection, legacy object detection, vision-based vehicle detection and tracking, and the like), medical image processing (object-of-interest region extraction), and the like. That is to say, the connected component analysis method can be used in an application scene in which a foreground object needs to be extracted for subsequent processing, and usually, the object of connected component analysis processing is a binarized image, which is the basis of all binary image analysis. The image to be retrieved needs to be converted into a binary image first. Connected Component (Connected Component) generally refers to an image area (Blob) composed of foreground pixels having the same pixel value and adjacent positions in an image. Connected Component Analysis (Connected Component Analysis) refers to finding and labeling each Connected region in an image.
There are many kinds of connected region labeling algorithms, which may be implemented by an algorithm used in a connected region labeling function bwleal in matlab, or by a labeling algorithm used in an open source library cvBlob, and this is not specifically limited in the embodiment of the present invention.
S140, acquiring preset information of the image block, and calculating a first preset variable value R1 and a second preset variable value R2 of the image block according to a predetermined calculation rule.
In this embodiment, the type (noise area or normal area) of the image block may be distinguished by obtaining preset information of the image block (the preset information includes a height value and a width value of a rectangular frame surrounding the image block, and a total number of white pixels in the image block), calculating a first preset variable value R1 and a second preset variable value R2 of the image block according to a predetermined calculation rule, and performing judgment and analysis on the calculated first preset variable value R1 and second preset variable value R2.
The predetermined calculation rule is: the first preset variable value R1= the area of the bright point in the block, and the second preset variable value R2= the area of the boundary in the block, wherein the area of the bright point in the block is the total number of the pixels in the binary area in the image block, and the area of the boundary in the block is the total number of the canny boundary pixels in the image block.
S150, judging the type of the image block, namely the image block is a noise area or a normal area.
In the present embodiment, the first preset variable value R1 and the second preset variable value R2 satisfy the first threshold condition (e.g., R2) by analyzing the first preset variable value R1 and the second preset variable value R2 for consistency1<40 and height H of the block<10) Judging the image block as a small noise area, displaying the image block in a first predetermined color (e.g., red), and if the predetermined variable value satisfies a second threshold condition (e.g., a second predetermined variable value R2/R)1<0.5 or R1Area per block>0.6), the image block is judged to be a large block noise and displayed in a second preset color (e.g., yellow). Therefore, the noise region and the normal region in the image can be visually distinguished.
In addition, the embodiment of the present invention further provides a computer-readable storage medium, which may be any one of or any combination of a hard disk, a multimedia card, an SD card, a flash memory card, an SMC, a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a portable compact disc read only memory (CD-ROM), a USB memory, and the like. The computer readable storage medium includes the image noise point identification program 10, and the specific implementation of the computer readable storage medium of the present invention is substantially the same as the above-mentioned image noise point identification method and the specific implementation of the server 1, and will not be described herein again.
It should be noted that the sequence of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
The above description of the embodiments of the present invention is for illustrative purposes only and does not represent the merits of the embodiments. Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (7)
1. An image noise point identification method is applied to a server, and is characterized by comprising the following steps:
an acquisition step: acquiring an image to be processed, and preprocessing the image;
the processing steps are as follows: extracting a binary boundary image of the preprocessed image according to a predetermined image edge detection algorithm, performing OTSU thresholding on the preprocessed image to obtain a binary region image, and performing line distance transformation on the binary region image to obtain at least one image block;
a marking step: analyzing a connected region of the image block to obtain a rectangular frame surrounding the image block so as to mark the position of the image block;
a calculation step: acquiring preset information of the image block, and calculating a first preset variable value R1 and a second preset variable value R2 of the image block according to a predetermined calculation rule, wherein the predetermined calculation rule is as follows:
the first preset variable value R1= the area of a bright point in a block, and the second preset variable value R2= the area of an inner boundary of the block, wherein the area of the bright point in the block is the total number of pixels in a binary area in the image block, and the area of the inner boundary of the block is the total number of canny boundary pixels in the image block; and
a judging step: and if the preset variable value meets a first threshold condition, judging the image block to be a noise area and displaying the image block in a first preset color, and if the preset variable value meets a second threshold condition, judging the image block to be a normal area and displaying the image block in a second preset color.
2. The method of image noise identification according to claim 1, wherein the image edge detection algorithm is a Canny algorithm.
3. The method of claim 1, wherein the predetermined information comprises a height value, a width value, a total number of binary region pixels in the image block, and a total number of canny boundary pixels in the image block of the rectangular frame surrounding the image block.
4. A server, comprising a memory and a processor, the memory having an image noise point identification program stored thereon, the image noise point identification program when executed by the processor performing the steps of:
an acquisition step: acquiring an image to be processed, and preprocessing the image;
the processing steps are as follows: extracting a binary boundary image of the preprocessed image according to a predetermined image edge detection algorithm, performing OTSU thresholding on the preprocessed image to obtain a binary region image, and performing line distance transformation on the binary region image to obtain at least one image block;
a marking step: analyzing a connected region of the image block to obtain a rectangular frame surrounding the image block so as to mark the position of the image block;
a calculation step: acquiring preset information of the image block, and calculating a first preset variable value R1 and a second preset variable value R2 of the image block according to a predetermined calculation rule, wherein the predetermined calculation rule is as follows:
the first preset variable value R1= the area of a bright point in a block, and the second preset variable value R2= the area of an inner boundary of the block, wherein the area of the bright point in the block is the total number of pixels in a binary area in the image block, and the area of the inner boundary of the block is the total number of canny boundary pixels in the image block; and
a judging step: and if the preset variable value meets a first threshold condition, judging the image block to be a noise area and displaying the image block in a first preset color, and if the preset variable value meets a second threshold condition, judging the image block to be a normal area and displaying the image block in a second preset color.
5. The server of claim 4, wherein the image edge detection algorithm is a Canny algorithm.
6. The server according to claim 5, wherein the preset information includes a height value, a width value, a total number of binary area pixels in the image block, and a total number of canny boundary pixels in the image block of the rectangular box surrounding the image block.
7. A computer readable storage medium having stored thereon an image noise identification program, the image noise identification program being executable by one or more processors to perform the steps of the image noise identification method according to any one of claims 1-3.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101794435A (en) * | 2010-03-04 | 2010-08-04 | 成都三泰电子实业股份有限公司 | Binary image noise-reduction method based on integral graph and binary image processing system |
| CN103942797A (en) * | 2014-04-24 | 2014-07-23 | 中国科学院信息工程研究所 | Scene image text detection method and system based on histogram and super-pixels |
| CN104103039A (en) * | 2013-04-08 | 2014-10-15 | 上海联影医疗科技有限公司 | Image noise estimation method |
| CN107609556A (en) * | 2017-10-09 | 2018-01-19 | 济南大学 | A kind of detection method of transmission line of electricity environment high and medium Work machine |
| CN108198146A (en) * | 2017-12-29 | 2018-06-22 | 努比亚技术有限公司 | A kind of noise-reduction method, equipment and computer readable storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10147017B2 (en) * | 2014-06-20 | 2018-12-04 | Qualcomm Incorporated | Systems and methods for obtaining structural information from a digital image |
| US10813614B2 (en) * | 2017-05-24 | 2020-10-27 | Perkinelmer Health Sciences, Inc. | Systems and methods for automated analysis of heterotopic ossification in 3D images |
-
2019
- 2019-04-01 CN CN201910258785.5A patent/CN110047083B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101794435A (en) * | 2010-03-04 | 2010-08-04 | 成都三泰电子实业股份有限公司 | Binary image noise-reduction method based on integral graph and binary image processing system |
| CN104103039A (en) * | 2013-04-08 | 2014-10-15 | 上海联影医疗科技有限公司 | Image noise estimation method |
| CN103942797A (en) * | 2014-04-24 | 2014-07-23 | 中国科学院信息工程研究所 | Scene image text detection method and system based on histogram and super-pixels |
| CN107609556A (en) * | 2017-10-09 | 2018-01-19 | 济南大学 | A kind of detection method of transmission line of electricity environment high and medium Work machine |
| CN108198146A (en) * | 2017-12-29 | 2018-06-22 | 努比亚技术有限公司 | A kind of noise-reduction method, equipment and computer readable storage medium |
Non-Patent Citations (3)
| Title |
|---|
| Identifying Immature Precursor Cells in Bone Marrow Pathological Images Based on Distance Transform and Internal Structures of Cells;Heng Wu等;《2010 international conference on multimedia technology》;20101111;第1-4页 * |
| 基于机器视觉的手写体区域检测和字符分割的研究;李晨;《优秀硕士论文集期刊》;20121225;第1-56页 * |
| 基于连通检测和区域扩展的多重聚类方法研究;钱涛 等;《航天电子对抗》;20161130(第5期);第36-39页 * |
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