CN111798392A - Object edge noise reduction method and system for infrared image - Google Patents
Object edge noise reduction method and system for infrared image Download PDFInfo
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- G06T7/10—Segmentation; Edge detection
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Abstract
The invention discloses an object edge noise reduction method and system for an infrared image, wherein the method comprises the following steps: step 1, obtaining an infrared image; step 2, carrying out frequency domain high-pass filtering on the infrared image, and then carrying out spatial low-pass filtering to obtain a first image; and 3, scanning the first image through an object edge detection algorithm and performing gray level smoothing to obtain an object edge image. The system comprises an infrared image acquisition unit, a first image acquisition unit and an object edge image acquisition unit. The invention is mainly used in the technical field of image processing.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an object edge noise reduction method and system for an infrared image.
Background
Biological images or thermal imaging images of other heating entities acquired by infrared image acquisition equipment are common, and collision detection of the biological images or the thermal imaging images is a common problem. For lakes, hills, regions with potential land falls and other areas with certain dangerousness or buildings with confidentiality requirements, corresponding infrared monitoring equipment is often required to be erected for all-weather monitoring of the buildings in order to prevent irrelevant people from entering the buildings unintentionally. Such infrared monitoring devices are often only a plurality of ordinary cameras erected at the periphery of the monitored area. Monitoring personnel confirm whether irrelevant personnel beneficially or unintentionally break into the infrared monitored area through the video data fed back by the plurality of cameras in real time in the control room. In the process of quickly identifying the infrared image, the edge of the object is generally quickly identified, but the edge of the object extracted by the existing object edge extraction algorithm has many problems, most notably many interference noise points, and the object edge image with many interference noise points is not beneficial to later-stage identification.
Disclosure of Invention
The present invention is directed to a method and system for object edge noise reduction of an infrared image, which solves one or more technical problems in the prior art and provides at least one useful choice or creation condition.
The technical scheme adopted for solving the technical problems is as follows: an object edge noise reduction method for an infrared image comprises the following steps:
step 1, obtaining an infrared image;
step 2, carrying out frequency domain high-pass filtering on the infrared image, and then carrying out spatial low-pass filtering to obtain a first image;
and 3, scanning the first image through an object edge detection algorithm and performing gray level smoothing to obtain an object edge image.
Further, in step 2, the frequency domain high-pass filtering the infrared image and then performing spatial low-pass filtering to obtain a first image specifically includes: the method comprises the steps of carrying out Fourier transform on an infrared image to obtain a frequency domain diagram of the infrared image, carrying out filtering processing on the frequency domain diagram by using a Gaussian high-pass filter to obtain a filtered frequency domain diagram, converting the filtered frequency domain diagram into a space domain diagram by using inverse Fourier transform, and filtering the space domain diagram by using the Gaussian low-pass filter to obtain a first image.
Further, in step 3, the first image is scanned by an object edge detection algorithm and gray level smoothing is performedThe object-edge image specifically includes: setting f (x, y) as an object edge image, setting h (x, y) as a Gaussian smoothing function, and convolving the object edge image with the Gaussian smoothing function to obtain an image g (x, y), namely:
calculating the second partial derivative of each pixel of the image g (x, y) in the x direction and the y direction
Then will be
After convolution with a gaussian smoothing function, performing second derivative processing once again to obtain an image k (x, y), specifically:
through the property of the zero crossing point of the second derivative, the modulus | k (x, y) | of the gradient of each pixel point can be obtained, when | k (x, y) | > T, the point can be regarded as the edge point of the edge image, the threshold value T is an integer, wherein T is more than or equal to 8 and less than or equal to 12.
Further, the obtaining the infrared image specifically includes: and shooting by an infrared camera to obtain an infrared image.
In another aspect, an object edge noise reduction system for infrared images includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the units of the following system:
the infrared image acquisition unit is used for acquiring an infrared image from external equipment and transmitting the infrared image to the first image acquisition unit;
the first image acquisition unit is used for carrying out frequency domain high-pass filtering on the infrared image and then carrying out spatial domain low-pass filtering to obtain a first image;
and the object edge image acquisition unit scans the first image through an object edge detection algorithm and performs gray level smoothing to obtain an object edge image.
Further, the first image acquisition unit includes: the device comprises a Fourier transform unit, an inverse Fourier transform unit, a Gaussian high-pass filter and a Gaussian low-pass filter;
the Fourier transform unit is used for carrying out Fourier transform on the infrared image to obtain a frequency domain graph of the infrared image and transmitting the frequency domain graph to a Gaussian high-pass filter;
the Gaussian high-pass filter is used for filtering the frequency domain graph to obtain a filtered frequency domain graph and transmitting the filtered frequency domain graph to the inverse Fourier transform unit;
the inverse Fourier transform unit is used for converting the filtered frequency domain diagram into a spatial domain diagram and transmitting the spatial domain diagram to a Gaussian low-pass filter;
the Gaussian low-pass filter is used for filtering the airspace map to obtain a first image.
Further, the object edge image acquisition unit is configured to: scanning the first image through an object edge detection algorithm and performing gray level smoothing to obtain an object edge image specifically comprises the following steps: setting f (x, y) as an object edge image, setting h (x, y) as a Gaussian smoothing function, and convolving the object edge image with the Gaussian smoothing function to obtain an image g (x, y), namely:
calculating the second partial derivative of each pixel of the image g (x, y) in the x direction and the y direction
Then will be
After convolution with a gaussian smoothing function, performing second derivative processing once again to obtain an image k (x, y), specifically:
through the property of the zero crossing point of the second derivative, the modulus | k (x, y) | of the gradient of each pixel point can be obtained, when | k (x, y) | > T, the point can be regarded as the edge point of the edge image, the threshold value T is an integer, wherein T is more than or equal to 8 and less than or equal to 12.
The invention has the beneficial effects that: on one hand, the object edge noise reduction method of the infrared image comprises the following steps: step 1, obtaining an infrared image; step 2, carrying out frequency domain high-pass filtering on the infrared image, and then carrying out spatial low-pass filtering to obtain a first image; and 3, scanning the first image through an object edge detection algorithm and performing gray level smoothing to obtain an object edge image. The obtained overall edge information of the object edge image is obvious, and the later edge identification is facilitated. On the other hand, there is provided a system that can perform an object edge noise reduction method of an infrared image, which has the advantageous effects of the object edge noise reduction method of an infrared image since it can perform the object edge noise reduction method of an infrared image, and thus will not be described repeatedly herein.
Drawings
The invention is further described with reference to the accompanying drawings and examples;
FIG. 1 is a flow chart of steps of a method of object edge noise reduction for infrared images;
FIG. 2 is a system unit connection diagram of an object edge noise reduction system for infrared images;
fig. 3 is a schematic block diagram of the first image capturing unit.
Detailed Description
Reference will now be made in detail to the present preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, if words such as "a plurality" are described, the meaning is one or more, the meaning of a plurality is two or more, more than, less than, more than, etc. are understood as excluding the present number, and more than, less than, etc. are understood as including the present number.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
Referring to fig. 1, an object edge noise reduction method for an infrared image includes:
step 1, obtaining an infrared image;
step 2, carrying out frequency domain high-pass filtering on the infrared image, and then carrying out spatial low-pass filtering to obtain a first image;
and 3, scanning the first image through an object edge detection algorithm and performing gray level smoothing to obtain an object edge image.
The method for obtaining the infrared image in step 1 is specifically to access an external device for obtaining through a communication mode, for example, to access a memory for obtaining the infrared image stored in the memory. After the infrared image is obtained, the infrared image can be processed as a target. In some preferred embodiments, the obtaining the infrared image specifically includes: and shooting by an infrared camera to obtain an infrared image.
In step 2, the principle of the treatment is as follows: the contrast between the high frequency and the low frequency of the target is firstly improved, the hidden noise is displayed, and then the hidden noise is removed on the premise of keeping the edge information of the target. Based on the principle, the specific processing method of the infrared image in the step 2 comprises the following steps: firstly, carrying out frequency domain high-pass filtering on the infrared image, and then carrying out spatial domain low-pass filtering. In the frequency domain, the infrared image can display the frequency information of the infrared image, so that the noise signal which cannot be perceived in the space domain can be reflected in the frequency domain, and the low-frequency part is weakened through the high-pass filtering of the frequency domain, so that the high-frequency part of the infrared image is more prominent, and the purpose of displaying the hidden noise signal is realized. And then removing the hidden noise signal through spatial domain low-pass filtering to reduce the overall noise on the premise of retaining the edge information.
In step 3, the obtained first image is scanned by an object edge detection algorithm, and the method is mainly used for delineating edge information in the first image so as to facilitate later-stage edge identification. Since there is a certain amount of noise generated after the edge information is drawn, in order to remove the noise, gradation smoothing processing is performed to obtain an object edge image. The obtained overall edge information of the object edge image is obvious, and the later edge identification is facilitated.
For frequency domain high pass filtering and spatial domain low pass filtering, a wide variety of filters may be employed, with some preferred embodiments employing a high pass filter. Therefore, in step 2, the frequency-domain high-pass filtering and then spatial-domain low-pass filtering the infrared image to obtain the first image specifically includes: the method comprises the steps of carrying out Fourier transform on an infrared image to obtain a frequency domain diagram of the infrared image, carrying out filtering processing on the frequency domain diagram by using a Gaussian high-pass filter to obtain a filtered frequency domain diagram, converting the filtered frequency domain diagram into a space domain diagram by using inverse Fourier transform, and filtering the space domain diagram by using the Gaussian low-pass filter to obtain a first image. Wherein, the template adopted by the Gaussian high-pass filter and the Gaussian low-pass filter is a standard template.
In step 3, scanning the first image through the object edge detection algorithm and performing gray level smoothing to obtain an object edge image specifically includes: setting f (x, y) as an object edge image, setting h (x, y) as a Gaussian smoothing function, and convolving the object edge image with the Gaussian smoothing function to obtain an image g (x, y), namely:
calculating each pixel of the image g (x, y) as two with respect to the x-direction and the y-directionThe partial derivative of the order is obtained
Then will be
After convolution with a gaussian smoothing function, performing second derivative processing once again to obtain an image k (x, y), specifically:
through the property of the zero crossing point of the second derivative, the modulus | k (x, y) | of the gradient of each pixel point can be obtained, when | k (x, y) | > T, the point can be regarded as the edge point of the edge image, the threshold value T is an integer, wherein T is more than or equal to 8 and less than or equal to 12.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
Referring to fig. 2 and 3, the present invention also provides an object edge noise reduction system for an infrared image, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the units of the following system:
the infrared image acquisition unit is used for acquiring an infrared image from external equipment and transmitting the infrared image to the first image acquisition unit;
the first image acquisition unit is used for carrying out frequency domain high-pass filtering on the infrared image and then carrying out spatial domain low-pass filtering to obtain a first image;
and the object edge image acquisition unit scans the first image through an object edge detection algorithm and performs gray level smoothing to obtain an object edge image.
Wherein the first image acquisition unit includes: the device comprises a Fourier transform unit, an inverse Fourier transform unit, a Gaussian high-pass filter and a Gaussian low-pass filter;
the Fourier transform unit is used for carrying out Fourier transform on the infrared image to obtain a frequency domain graph of the infrared image and transmitting the frequency domain graph to a Gaussian high-pass filter;
the Gaussian high-pass filter is used for filtering the frequency domain graph to obtain a filtered frequency domain graph and transmitting the filtered frequency domain graph to the inverse Fourier transform unit;
the inverse Fourier transform unit is used for converting the filtered frequency domain diagram into a spatial domain diagram and transmitting the spatial domain diagram to a Gaussian low-pass filter;
the Gaussian low-pass filter is used for filtering the airspace map to obtain a first image.
The object edge image acquisition unit is used for: scanning the first image through an object edge detection algorithm and performing gray level smoothing to obtain an object edge image specifically comprises the following steps: let f (x, y) be the object edgeAn edge image, h (x, y) is a gaussian smoothing function, and the object edge image is convolved with the gaussian smoothing function to obtain an image g (x, y), namely:
calculating the second partial derivative of each pixel of the image g (x, y) in the x direction and the y direction
Then will be
After convolution with a gaussian smoothing function, performing second derivative processing once again to obtain an image k (x, y), specifically:
through the property of the zero crossing point of the second derivative, the modulus | k (x, y) | of the gradient of each pixel point can be obtained, when | k (x, y) | > T, the point can be regarded as the edge point of the edge image, the threshold value T is an integer, wherein T is more than or equal to 8 and less than or equal to 12.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center for the object edge noise reduction system for the one infrared image, the various portions of the system being operable by the object edge noise reduction system for the entire one infrared image being connected by various interfaces and lines.
The memory may be used to store the computer program and/or module, and the processor may implement the various functions of the object edge noise reduction system for an infrared image by running or executing the computer program and/or module stored in the memory and invoking the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that the present invention is not limited to the details of the embodiments shown and described, but is capable of numerous equivalents and substitutions without departing from the spirit of the invention as set forth in the claims appended hereto.
Claims (7)
1. An object edge noise reduction method for an infrared image is characterized by comprising the following steps: the method comprises the following steps:
step 1, obtaining an infrared image;
step 2, carrying out frequency domain high-pass filtering on the infrared image, and then carrying out spatial low-pass filtering to obtain a first image;
and 3, scanning the first image through an object edge detection algorithm and performing gray level smoothing to obtain an object edge image.
2. The method of claim 1, wherein the method comprises: in step 2, the frequency domain high-pass filtering the infrared image and then performing spatial low-pass filtering to obtain a first image specifically includes: the method comprises the steps of carrying out Fourier transform on an infrared image to obtain a frequency domain diagram of the infrared image, carrying out filtering processing on the frequency domain diagram by using a Gaussian high-pass filter to obtain a filtered frequency domain diagram, converting the filtered frequency domain diagram into a space domain diagram by using inverse Fourier transform, and filtering the space domain diagram by using the Gaussian low-pass filter to obtain a first image.
3. The method of claim 1, wherein the method comprises: in step 3, scanning the first image through the object edge detection algorithm and performing gray level smoothing to obtain an object edge image specifically includes: setting f (x, y) as an object edge image, setting h (x, y) as a Gaussian smoothing function, and convolving the object edge image with the Gaussian smoothing function to obtain an image g (x, y), namely:
calculating the second partial derivative of each pixel of the image g (x, y) in the x direction and the y direction
Then will be
After convolution with a gaussian smoothing function, performing second derivative processing once again to obtain an image k (x, y), specifically:
through the property of the zero crossing point of the second derivative, the modulus | k (x, y) | of the gradient of each pixel point can be obtained, when | k (x, y) | > T, the point can be regarded as the edge point of the edge image, the threshold value T is an integer, wherein T is more than or equal to 8 and less than or equal to 12.
4. The method of claim 1, wherein the method comprises: the obtaining of the infrared image specifically includes: and shooting by an infrared camera to obtain an infrared image.
5. An object edge noise reduction system for infrared images, characterized by: the method comprises the following steps: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the units of the following system:
the infrared image acquisition unit is used for acquiring an infrared image from external equipment and transmitting the infrared image to the first image acquisition unit;
the first image acquisition unit is used for carrying out frequency domain high-pass filtering on the infrared image and then carrying out spatial domain low-pass filtering to obtain a first image;
and the object edge image acquisition unit scans the first image through an object edge detection algorithm and performs gray level smoothing to obtain an object edge image.
6. The system of claim 5, wherein: the first image acquisition unit includes: the device comprises a Fourier transform unit, an inverse Fourier transform unit, a Gaussian high-pass filter and a Gaussian low-pass filter;
the Fourier transform unit is used for carrying out Fourier transform on the infrared image to obtain a frequency domain graph of the infrared image and transmitting the frequency domain graph to a Gaussian high-pass filter;
the Gaussian high-pass filter is used for filtering the frequency domain graph to obtain a filtered frequency domain graph and transmitting the filtered frequency domain graph to the inverse Fourier transform unit;
the inverse Fourier transform unit is used for converting the filtered frequency domain diagram into a spatial domain diagram and transmitting the spatial domain diagram to a Gaussian low-pass filter;
the Gaussian low-pass filter is used for filtering the airspace map to obtain a first image.
7. The system of claim 5, wherein: the object edge image acquisition unit is configured to: scanning the first image by an object edge detection algorithm and performing gray level smoothing to obtainThe object-edge image specifically includes: setting f (x, y) as an object edge image, setting h (x, y) as a Gaussian smoothing function, and convolving the object edge image with the Gaussian smoothing function to obtain an image g (x, y), namely:
calculating the second partial derivative of each pixel of the image g (x, y) in the x direction and the y direction
Then will be
After convolution with a gaussian smoothing function, performing second derivative processing once again to obtain an image k (x, y), specifically:
through the property of the zero crossing point of the second derivative, the modulus | k (x, y) | of the gradient of each pixel point can be obtained, when | k (x, y) | > T, the point can be regarded as the edge point of the edge image, the threshold value T is an integer, wherein T is more than or equal to 8 and less than or equal to 12.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102592263A (en) * | 2011-01-05 | 2012-07-18 | 苏州巴米特信息科技有限公司 | Image reinforcement method based on frequency domain |
| US20180114296A1 (en) * | 2016-10-21 | 2018-04-26 | Qualcomm Incorporated | Systems and methods for image enhancement |
| CN109215018A (en) * | 2018-08-23 | 2019-01-15 | 上海海事大学 | Based on Canny operator and the morphologic ship detecting method of Gauss |
| CN109684926A (en) * | 2018-11-21 | 2019-04-26 | 佛山市第一人民医院（中山大学附属佛山医院） | Non-contact vein image acquisition method and device |
-
2020
- 2020-06-29 CN CN202010611404.XA patent/CN111798392A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102592263A (en) * | 2011-01-05 | 2012-07-18 | 苏州巴米特信息科技有限公司 | Image reinforcement method based on frequency domain |
| US20180114296A1 (en) * | 2016-10-21 | 2018-04-26 | Qualcomm Incorporated | Systems and methods for image enhancement |
| CN109215018A (en) * | 2018-08-23 | 2019-01-15 | 上海海事大学 | Based on Canny operator and the morphologic ship detecting method of Gauss |
| CN109684926A (en) * | 2018-11-21 | 2019-04-26 | 佛山市第一人民医院（中山大学附属佛山医院） | Non-contact vein image acquisition method and device |
Non-Patent Citations (1)
| Title |
|---|
| 贾文晶等: "基于高通滤波和直方图均衡的钢轨裂纹红外图像增强技术", 《铁道标准设计》 * |
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Application publication date: 20201020 |