CN117314791A - Infrared image cold reflection noise correction method based on Butterworth function fitting - Google Patents
Infrared image cold reflection noise correction method based on Butterworth function fitting Download PDFInfo
- Publication number
- CN117314791A CN117314791A CN202311588699.3A CN202311588699A CN117314791A CN 117314791 A CN117314791 A CN 117314791A CN 202311588699 A CN202311588699 A CN 202311588699A CN 117314791 A CN117314791 A CN 117314791A
- Authority
- CN
- China
- Prior art keywords
- image
- infrared image
- fitting
- cold reflection
- function
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 70
- 238000012937 correction Methods 0.000 title claims abstract description 28
- 238000013507 mapping Methods 0.000 claims abstract description 10
- 230000009466 transformation Effects 0.000 claims abstract description 4
- 230000006870 function Effects 0.000 claims description 59
- 238000012545 processing Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 6
- 230000001131 transforming effect Effects 0.000 claims description 6
- 238000013178 mathematical model Methods 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000000717 retained effect Effects 0.000 claims description 3
- 230000005764 inhibitory process Effects 0.000 claims description 2
- 238000009499 grossing Methods 0.000 abstract description 3
- 230000002401 inhibitory effect Effects 0.000 abstract 1
- 230000001360 synchronised effect Effects 0.000 description 3
- 238000000605 extraction Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000001629 suppression Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003703 image analysis method Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10048—Infrared image
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Algebra (AREA)
- Computational Mathematics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Analysis (AREA)
- Image Processing (AREA)
- Transforming Light Signals Into Electric Signals (AREA)
Abstract
The invention provides an infrared image cold reflection noise correction method based on Butterworth function fitting, which comprises the steps of firstly designing a smoothing window to carry out smoothing treatment on infrared image data, removing redundant scene information, retaining cold reflection signals, carrying out inverse transformation on the redundant scene information, retaining information around cold reflection to the maximum extent, and inhibiting and eliminating other information so as to minimize the image scene information; and then designing a two-dimensional Butterworth function, initializing parameters to be fitted, fitting by using a least square method, finally extracting parameters of the fitting function as characteristics of intercooling reflection in the infrared image, fitting a curved surface formed after mapping by using the two-dimensional Butterworth function, using geometric characteristics of the two-dimensional Butterworth function to represent the characteristics of intercooling reflection in the infrared image, and removing noise in the image by differentiating the geometric characteristics with the infrared image, so that the image can be clearly distinguished from the background, and the quality of the infrared image is improved.
Description
Technical Field
The invention belongs to the technical field of infrared image denoising, and particularly relates to an infrared image cold reflection noise correction method based on Butterworth function fitting.
Background
Infrared images have been widely used in the fields of optical measurement, object recognition, national defense and military industry and other related national lives, however, the infrared detector is inevitably affected by noise in the imaging process, and cold reflection phenomenon is taken as a defect in the infrared images, so that the visibility of the infrared images is affected, and the subsequent processing applications such as object detection, recognition and tracking of the infrared images are affected.
The current non-uniformity correction technology is divided into a calibration-based method and a scene-based correction method, and the methods have a certain noise suppression effect, but have no obvious effect on improving the quality of infrared images because serious artifact phenomenon can be generated after noise caused by cold reflection is processed.
Disclosure of Invention
In order to solve the problems, the invention provides an infrared image cold reflection noise correction method based on Butterworth function fitting, in particular to an infrared image denoising method based on Butterworth curve fitting, which can inhibit the influence caused by cold reflection and improve the signal-to-noise ratio of an infrared image in a low-temperature environment so as to remove noise in the image.
The invention is realized by the following technical scheme:
an infrared image cold reflection noise correction method based on Butterworth function fitting comprises the following steps:
step one, reading an infrared image through a sensor；
Step two, designing a smooth window to read the infrared image in the step oneCarrying out smooth pretreatment; removing redundant scene information, inversely transforming the scene information to keep information around cold reflection to the maximum extent, and simultaneously suppressing and eliminating other information to minimize the image scene information;
step three, constructing a two-dimensional Butterworth function, and initializing parameters to be fitted;
fitting the data by using a least square method, and finally extracting parameters of a fitting function as characteristics of cold reflection in the infrared image;
fitting the curved surface formed after mapping by using a two-dimensional Butterworth function, using the geometric features of the two-dimensional Butterworth function to represent the features of cold reflection in the infrared image, and removing noise in the image by differentiating the geometric features with the infrared image to finish the correction of the cold reflection noise of the infrared image.
Further, in the second step, a smoothing window is designed to process a local area of the infrared image so as to reduce the prominent noise point or abnormal value;
the pretreatment specifically adopts the following formula:
the preprocessing algorithm is as follows:；
wherein,representing image information +.>Representing the maximum gray value of the mean image orMaximum value of>Information pixel of the representation +.>And cold reflection signal center->The distance between，/>The standard deviation in the mean image, i.e., the extent to which the scene signal in the image is suppressed;
for infrared imagesPreprocessing to smooth image, wherein the smoothed image is defined as +.>。
Further, in the second step, the details lost in the smoothed process are rebuilt through the inverse transformation, so that important characteristics of the cold reflection signal are ensured not to be lost due to processing;
and eliminates information irrelevant to cold reflection in the image through information inhibition to ensure the smoothed imageOnly critical information is retained.
Further, in a third step, the process comprises,
definition of two-dimensional Butterworth function；
In the middle ofRepresents amplitude->Representing attributes, & lt->Mean value of the representation attribute>Representing the standard deviation of the properties.
Further, in a fourth step, the first step,
will beAll initialized to 1; adjusting +.>And knowing that the fitting precision reaches a threshold value, and acquiring the fitted Butterworth fitting parameters, wherein the Butterworth fitting parameters are regarded as cold reflection characteristic parameters in the infrared image.
Further, in the fifth step, specifically, the method includes:
fitting the curved surface formed after the mapping in the step two by using the Butterworth function obtained by the least square method in the step four as a mathematical model so as to capture and represent the geometric characteristics of the cold reflection signals;
and using the geometric features of the two-dimensional butterworth function to represent the features of cold reflection in the infrared image: and removing noise in the image by differentiating the fitted Butterworth function with the original infrared image.
Obtaining the result of the step five, extractingInfrared image comprising cold reflection noise +.>Is characterized in that the correction of the infrared image cold reflection noise is realized.
An infrared image cold reflection noise correction system based on Butterworth function fitting:
the corrective system includes: the device comprises an image processing module, a fitting module and a correction module;
the image processing module reads an infrared imageThe method comprises the steps of carrying out a first treatment on the surface of the And designing a smooth window for the read infrared image +.>Carrying out smooth pretreatment; removing redundant scene information, inversely transforming the scene information to keep information around cold reflection to the maximum extent, and simultaneously suppressing and eliminating other information to minimize the image scene information;
the fitting module constructs a two-dimensional Butterworth function, and initializes parameters to be fitted; fitting the data by using a least square method, and finally extracting parameters of a fitting function as characteristics of cold reflection in the infrared image;
the correction module fits the curved surface formed after mapping by using a two-dimensional Butterworth function, uses the geometric features of the two-dimensional Butterworth function to represent the features of cold reflection in the infrared image, and removes noise in the image by differentiating the features with the infrared image, so that the correction of the cold reflection noise of the infrared image is completed.
An electronic device comprising a memory storing a computer program and a processor implementing the steps of the above method when the processor executes the computer program.
A computer readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the above method.
The invention has the beneficial effects that
The invention combines the existing infrared noise image denoising method to analyze the infrared image from the noise distribution angle. And utilizing the two-dimensional Butterworth function pair to map the formed curved surface, and representing the infrared image data characteristics by using the geometric characteristics of the two-dimensional Butterworth function pair.
The method carries out two-dimensional Butterworth surface fitting and denoising treatment on the infrared image. Compared with other infrared image analysis methods, the method can obviously improve the signal-to-noise ratio of the infrared image, thereby removing noise in the image and improving the quality of the infrared image.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
An infrared image cold reflection noise correction method based on Butterworth function fitting comprises the following steps:
step one, reading an infrared image by a sensor or other device；
Step two, designing a smooth window to read the infrared image in the step oneCarrying out smooth pretreatment; removing redundant scene information, inversely transforming the scene information to keep information around cold reflection to the maximum extent, and simultaneously suppressing and eliminating other information to minimize the image scene information; so that the cold reflected signal in the original image is more pronounced.
In the second step, a smooth window is designed to process the local area of the infrared image so as to reduce the prominent noise point or abnormal value;
the pretreatment specifically adopts the following formula:
the preprocessing algorithm is as follows:；
wherein,representing image information +.>Representing the maximum gray value of the mean image orMaximum value of>Information pixel of the representation +.>And cold reflection signal center->The distance between，/>The standard deviation in the mean image, i.e., the extent to which the scene signal in the image is suppressed;
for infrared imagesPreprocessing to smooth image, wherein the smoothed image is defined as +.>。
In the second step, reconstructing the lost details in the smoothed process through the inverse transformation, so as to ensure that the important characteristics of the cold reflection signal are not lost due to processing;
and eliminating information irrelevant to cold reflection in the image by information suppression (realized by specific filter or processing technology) to ensure the smoothed imageOnly keyIs retained in order to better highlight the cold reflected signal.
Step three, constructing a two-dimensional Butterworth function, and initializing parameters to be fitted;
in the third step of the process, the process is carried out,
definition of two-dimensional Butterworth function；
In the middle ofRepresents amplitude->Representing attributes, & lt->Mean value of the representation attribute>Representing the standard deviation of the properties.
Fitting the data by using a least square method, and finally extracting parameters of a fitting function as characteristics of cold reflection in the infrared image to realize the extraction of noise characteristics of the infrared image;
in step four, in the fitting process, the parameters of the butterworth function are adjusted by minimizing the error between the fitting function and the actual data, so that the function fits the cold reflection signal in the infrared image better, and the least square method tries to continuously adjust the parameters of the butterworth function until the state of minimizing the error is reached; after the fitting is completed, the parameters of the resulting butterworth function will be regarded as a representation of the cold reflection features in the infrared image.
Will beAll initialized to 1; adjusting +.>Knowing that fitting precision reaches a threshold value, obtaining a fittingAnd (3) a butterworth fitting parameter after combination, wherein the butterworth fitting parameter is regarded as cold reflection characteristic parameter in the infrared image.
I.e. in step four, the parameters of the butterworth function have been adjusted to best fit the cold reflected signal in the original infrared image by the least squares method. The butterworth function is a mathematical model in which the parameters are obtained by a fitting process.
Fitting the curved surface formed after mapping by using a two-dimensional Butterworth function, using the geometric features of the two-dimensional Butterworth function to represent the cold reflection features in the infrared image, and removing noise in the image by differentiating the geometric features with the infrared image, so that the image can be clearly distinguished from the background, the infrared image quality is improved, and the cold reflection noise correction of the infrared image is completed.
The fifth step specifically comprises:
fitting the curved surface formed after the mapping in the step two by using the Butterworth function obtained by the least square method in the step four as a mathematical model so as to capture and represent the geometric characteristics of the cold reflection signals;
and using the geometric features of the two-dimensional butterworth function to represent the features of cold reflection in the infrared image: and removing noise in the image by differentiating the fitted Butterworth function with the original infrared image.
This is because the fitted function captures mainly the characteristics of the cold reflected signal, whereas the signal that is not related to the cold reflection is removed, thereby improving the quality of the image. Through the above differential operation, noise is removed, so that the image is clearer. The geometry of the fitted butterworth function helps to distinguish the cold reflected signal from the image background, making the target signal more prominent. In summary, the overall objective is to improve the quality of the infrared image while the correction of the cold reflection noise is accomplished by fitting the butterworth function and the differential operation.
After these steps are completed, the obtained infrared image should be clearer, the cold reflection signal is more prominent, and the noise is effectively removed.
Obtaining the result of the step five, extractingInfrared image comprising cold reflection noise +.>Is characterized in that the correction of the infrared image cold reflection noise is realized.
An infrared image cold reflection noise correction system based on Butterworth function fitting:
the corrective system includes: the device comprises an image processing module, a fitting module and a correction module;
the image processing module reads the infrared image through a sensor or other devicesThe method comprises the steps of carrying out a first treatment on the surface of the And designing a smooth window for the read infrared image +.>Carrying out smooth pretreatment; removing redundant scene information, inversely transforming the scene information to keep information around cold reflection to the maximum extent, and simultaneously suppressing and eliminating other information to minimize the image scene information; so that the cold reflected signal in the original image is more pronounced.
The fitting module constructs a two-dimensional Butterworth function, and initializes parameters to be fitted; fitting the data by using a least square method, and finally extracting parameters of a fitting function as characteristics of cold reflection in the infrared image to realize the extraction of noise characteristics of the infrared image;
the correction module fits the curved surface formed after mapping by using a two-dimensional Butterworth function, uses the geometric feature of the two-dimensional Butterworth function to represent the feature of cold reflection in the infrared image, and removes noise in the image by differentiating the geometric feature with the infrared image, so that the image can be clearly distinguished from the background, the quality of the infrared image is improved, and the correction of the cold reflection noise of the infrared image is completed.
An electronic device comprising a memory storing a computer program and a processor implementing the steps of the above method when the processor executes the computer program.
A computer readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the above method.
The memory in embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be read only memory, ROM, programmable ROM, PROM, erasable PROM, EPROM, electrically erasable EPROM, EEPROM, or flash memory. The volatile memory may be random access memory random access memory, RAM, which acts as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM, SRAM, dynamic RAM, DRAM, synchronous DRAM, SDRAM, double data rate synchronous DRAM double data rate SDRAM, DDR SDRAM, enhanced SDRAM, ESDRAM, synchronous link DRAM, SLDRAM and direct memory bus RAM, DR RAM. It should be noted that the memory of the methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by means of a wired, such as coaxial cable, optical fiber, digital subscriber line digital subscriber line, DSL, or wireless, such as infrared, wireless, microwave, or the like. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium such as a floppy disk, a hard disk, a magnetic tape, an optical medium such as a high-density digital video disk digital video disc, a DVD, or a semiconductor medium such as a solid state disk, an SSD, or the like.
In implementation, each step of the method may be implemented by an integrated logic circuit of hardware in a processor or an instruction in a form of a software component. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
It should be noted that the processor in the embodiments of the present application may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by integrated logic circuits of hardware in a processor or instructions in software form. The processor may be a general purpose processor, a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The above describes the method for correcting the cold reflection noise of the infrared image based on the Butterworth function fitting, and the principle and the implementation mode of the invention are described in detail, and the description of the above examples is only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (9)
1. An infrared image cold reflection noise correction method based on Butterworth function fitting is characterized by comprising the following steps of:
step one, reading an infrared image through a sensor；
Step two, designing a smooth window to read the infrared image in the step oneCarrying out smooth pretreatment; removing redundant scene information, inversely transforming the scene information to keep information around cold reflection to the maximum extent, and simultaneously suppressing and eliminating other information to minimize the image scene information;
step three, constructing a two-dimensional Butterworth function, and initializing parameters to be fitted;
fitting the data by using a least square method, and finally extracting parameters of a fitting function as characteristics of cold reflection in the infrared image;
fitting the curved surface formed after mapping by using a two-dimensional Butterworth function, using the geometric features of the two-dimensional Butterworth function to represent the features of cold reflection in the infrared image, and removing noise in the image by differentiating the geometric features with the infrared image to finish the correction of the cold reflection noise of the infrared image.
2. The method according to claim 1, wherein:
in the second step, a smooth window is designed to process the local area of the infrared image so as to reduce the prominent noise point or abnormal value;
the pretreatment specifically adopts the following formula:
the preprocessing algorithm is as follows:；
wherein,representing image information +.>Representing the maximum gray value of the mean image or +.>Maximum value of>Information pixel of the representation +.>And cold reflection signal center->The distance between，/>The standard deviation in the mean image, i.e., the extent to which the scene signal in the image is suppressed;
for infrared imagesPreprocessing to smooth image, wherein the smoothed image is defined as +.>。
3. The method according to claim 2, characterized in that:
in the second step, reconstructing the details lost in the smoothed process through inverse transformation, so as to ensure that important characteristics of the cold reflection signals are not lost due to processing;
and eliminates information irrelevant to cold reflection in the image through information inhibition to ensure the smoothed imageOnly critical information is retained.
4. A method according to claim 3, characterized in that: in the third step of the process, the process is carried out,
definition of two-dimensional Butterworth function；
In the middle ofRepresents amplitude->Representing attributes, & lt->Mean value of the representation attribute>Representing the standard deviation of the properties.
5. The method according to claim 4, wherein: in the fourth step of the process, the process is carried out,
will beAll initialized to 1; adjusting +.>And knowing that the fitting precision reaches a threshold value, and acquiring the fitted Butterworth fitting parameters, wherein the Butterworth fitting parameters are regarded as cold reflection characteristic parameters in the infrared image.
6. The method according to claim 5, wherein:
the fifth step specifically comprises:
fitting the curved surface formed after the mapping in the step two by using the Butterworth function obtained by the least square method in the step four as a mathematical model so as to capture and represent the geometric characteristics of the cold reflection signals;
and using the geometric features of the two-dimensional butterworth function to represent the features of cold reflection in the infrared image: the noise in the image is removed by differentiating the fitted Butterworth function with the original infrared image;
obtaining the result of the step five, extractingInfrared image comprising cold reflection noise +.>Is characterized by realizing cold reflection noise of infrared imageCorrecting.
7. An infrared image cold reflection noise correction system based on Butterworth function fitting is characterized in that:
the corrective system includes: the device comprises an image processing module, a fitting module and a correction module;
the image processing module reads an infrared imageThe method comprises the steps of carrying out a first treatment on the surface of the And designing a smooth window for the read infrared image +.>Carrying out smooth pretreatment; removing redundant scene information, inversely transforming the scene information to keep information around cold reflection to the maximum extent, and simultaneously suppressing and eliminating other information to minimize the image scene information;
the fitting module constructs a two-dimensional Butterworth function, and initializes parameters to be fitted; fitting the data by using a least square method, and finally extracting parameters of a fitting function as characteristics of cold reflection in the infrared image;
the correction module fits the curved surface formed after mapping by using a two-dimensional Butterworth function, uses the geometric features of the two-dimensional Butterworth function to represent the features of cold reflection in the infrared image, and removes noise in the image by differentiating the features with the infrared image, so that the correction of the cold reflection noise of the infrared image is completed.
8. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311588699.3A CN117314791B (en) | 2023-11-27 | 2023-11-27 | Infrared image cold reflection noise correction method based on Butterworth function fitting |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311588699.3A CN117314791B (en) | 2023-11-27 | 2023-11-27 | Infrared image cold reflection noise correction method based on Butterworth function fitting |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117314791A true CN117314791A (en) | 2023-12-29 |
| CN117314791B CN117314791B (en) | 2024-02-20 |
Family
ID=89281400
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311588699.3A Active CN117314791B (en) | 2023-11-27 | 2023-11-27 | Infrared image cold reflection noise correction method based on Butterworth function fitting |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117314791B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103592367A (en) * | 2013-10-23 | 2014-02-19 | 江苏大学 | Portable poultry egg quality detection device and method |
| US20170019614A1 (en) * | 2014-03-06 | 2017-01-19 | Nec Corporation | Image capturing device, signal separation device, and image capturing method |
| CN109035362A (en) * | 2018-06-11 | 2018-12-18 | 西安电子科技大学 | Cold emission removing method based on cold emission strength model |
| CN109191387A (en) * | 2018-07-20 | 2019-01-11 | 河南师范大学 | A kind of Infrared Image Denoising method based on Butterworth filter |
| CN109829861A (en) * | 2018-12-29 | 2019-05-31 | 西安电子科技大学 | A kind of cold emission suppressing method based on wavelet decomposition |
| US20190206043A1 (en) * | 2017-12-29 | 2019-07-04 | Huizhou China Star Optoelectronics Technology Co., Ltd. | Method and system for detection of in-panel mura based on hough transform and gaussian fitting |
| CN111738933A (en) * | 2020-05-15 | 2020-10-02 | 南京邮电大学 | Noise suppression method and device in infrared digital holographic reconstruction |
| CN113326855A (en) * | 2021-06-22 | 2021-08-31 | 长光卫星技术有限公司 | Night lamplight remote sensing image feature extraction method based on two-dimensional Gaussian surface fitting |
-
2023
- 2023-11-27 CN CN202311588699.3A patent/CN117314791B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103592367A (en) * | 2013-10-23 | 2014-02-19 | 江苏大学 | Portable poultry egg quality detection device and method |
| US20170019614A1 (en) * | 2014-03-06 | 2017-01-19 | Nec Corporation | Image capturing device, signal separation device, and image capturing method |
| US20190206043A1 (en) * | 2017-12-29 | 2019-07-04 | Huizhou China Star Optoelectronics Technology Co., Ltd. | Method and system for detection of in-panel mura based on hough transform and gaussian fitting |
| CN109035362A (en) * | 2018-06-11 | 2018-12-18 | 西安电子科技大学 | Cold emission removing method based on cold emission strength model |
| CN109191387A (en) * | 2018-07-20 | 2019-01-11 | 河南师范大学 | A kind of Infrared Image Denoising method based on Butterworth filter |
| CN109829861A (en) * | 2018-12-29 | 2019-05-31 | 西安电子科技大学 | A kind of cold emission suppressing method based on wavelet decomposition |
| CN111738933A (en) * | 2020-05-15 | 2020-10-02 | 南京邮电大学 | Noise suppression method and device in infrared digital holographic reconstruction |
| CN113326855A (en) * | 2021-06-22 | 2021-08-31 | 长光卫星技术有限公司 | Night lamplight remote sensing image feature extraction method based on two-dimensional Gaussian surface fitting |
Non-Patent Citations (1)
| Title |
|---|
| 崔帅;刘波;丁德红;: "基于傅里叶变换的森林红外图像增强算法研究", 红外技术, no. 01 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117314791B (en) | 2024-02-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021217643A1 (en) | Method and device for infrared image processing, and movable platform | |
| CN107749987B (en) | Digital video image stabilization method based on block motion estimation | |
| US9443286B2 (en) | Gray image processing method and apparatus based on wavelet transformation | |
| JP6446374B2 (en) | Improvements in image processing or improvements related to image processing | |
| WO2012060093A1 (en) | Stereo image processing device and stereo image processing method | |
| CN112508816B (en) | Infrared image sharpening method, sharpening processing system and terminal equipment | |
| CN108305268B (en) | Image segmentation method and device | |
| CN112037287A (en) | Camera calibration method, electronic device and storage medium | |
| CN112435181A (en) | Method for filtering vertical stripe noise of uncooled infrared video image | |
| CN107451986B (en) | Single infrared image enhancement method based on fusion technology | |
| CN106651981B (en) | Method and device for correcting ring artifact | |
| CN117314791B (en) | Infrared image cold reflection noise correction method based on Butterworth function fitting | |
| CN113744294A (en) | Image processing method and related device | |
| CN111415365B (en) | Image detection method and device | |
| WO2021134642A1 (en) | Image processing method and apparatus, and storage medium | |
| Wang et al. | An improved Canny algorithm with adaptive threshold selection | |
| CN115311532A (en) | Ground penetrating radar underground cavity target automatic identification method based on ResNet network model | |
| CN111815670A (en) | Multi-view target tracking method, device and system, electronic terminal and storage medium | |
| CN112767242B (en) | Image processing method and device based on frequency domain deconvolution and electronic equipment | |
| CN112927169A (en) | Remote sensing image denoising method based on wavelet transformation and improved weighted nuclear norm minimization | |
| CN113269687A (en) | Non-local average infrared image denoising method based on parameter optimization | |
| CN115880188B (en) | Polarization direction statistical image generation method, device and medium | |
| Dong et al. | Traditional pattern enhancement based on improved singular value decomposition and gamma function in the frequency domain | |
| CN116051425B (en) | Infrared image processing method and device, electronic equipment and storage medium | |
| CN116433535B (en) | Point coordinate de-distortion method, system and storage medium for quadratic curve fitting |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |