CN117314791B - Infrared image cold reflection noise correction method based on Butterworth function fitting - Google Patents

Abstract

The present invention proposes a cold reflection noise correction method for infrared images based on Butterworth function fitting. First, a smoothing window is designed to smooth the infrared image data, remove redundant scene information while retaining the cold reflection signal, and inversely transform it to maximize the The information around the cold reflection is retained to the maximum extent, while the information other than it is suppressed and eliminated to minimize the image scene information; then a two-dimensional Butterworth function is designed, the parameters that need to be fitted are initialized, and the least squares method is used for fitting. Finally, the parameters of the fitting function are extracted as the characteristics of cold reflection in the infrared image, the two-dimensional Butterworth function is used to fit the surface formed after mapping, and the geometric characteristics of the two-dimensional Butterworth function are used to represent the characteristics of the cold reflection in the infrared image. Features, through difference with the infrared image to remove noise in the image, can clearly distinguish the image from the background and improve the quality of the infrared image.

Description

Infrared image cold reflection noise correction method based on Butterworth function fitting

Technical field

The invention belongs to the technical field of infrared image denoising, and specifically relates to a method for correcting cold reflection noise of infrared images based on Butterworth function fitting.

Background technique

Infrared images have been widely used in a series of aspects related to the national economy and people's livelihood, such as optical measurement, target recognition, national defense and military industry. However, infrared detectors will inevitably be affected by noise during the imaging process. The cold reflection phenomenon is one of the main problems in infrared images. A defect that not only affects the visibility of infrared images, but also affects subsequent processing applications such as target detection, identification and tracking of infrared images.

The current non-uniformity correction technology is divided into calibration-based methods and scene-based correction methods. These methods have a certain inhibitory effect on noise, but the noise caused by cold reflection will produce serious artifacts after processing, which will cause serious artifacts. The effect of improving the quality of infrared images is not obvious.

Contents of the invention

In response to the above problems, the present invention proposes a method for correcting cold reflection noise in infrared images based on Butterworth function fitting. Specifically, infrared image denoising is based on Butterworth surface fitting, which can suppress the impact of cold reflection and improve The signal-to-noise ratio of infrared images in low temperature environments, thereby removing noise in the images.

The present invention is realized through the following technical solutions:

A method for correcting cold reflection noise in infrared images based on Butterworth function fitting:

Step 1. Read the infrared image through the sensor ;

Step 2. Design a smoothing window for the infrared image read in step 1. Perform smoothing preprocessing; remove excess scene information and inversely transform it to retain the information around the cold reflection to the maximum extent, while suppressing and eliminating information other than it to minimize the image scene information;

Step 3: Construct a two-dimensional Butterworth function and initialize the parameters that need to be fitted;

Step 4: Use the least squares method to fit the data, and finally extract the parameters of the fitting function as the characteristics of cold reflection in the infrared image;

Step 5: Use the two-dimensional Butterworth function to fit the surface formed after mapping, use the geometric characteristics of the two-dimensional Butterworth function to represent the characteristics of cold reflection in the infrared image, and remove the noise in the image by difference with the infrared image. , complete the cold reflection noise correction of infrared images.

Further, in step two, a smoothing window is designed to process local areas of the infrared image to reduce prominent noise points or outliers;

The pretreatment specifically adopts the following formula:

The preprocessing algorithm is: ;

in, Represents image information,/> It represents the maximum gray value of the mean image or The maximum value,/> Represented information pixels/> with cold reflection signal center/> The distance between ,/> It represents the standard deviation in the mean image, which is the degree of suppression of the scene signal in the image;

to infrared images Perform preprocessing to achieve the purpose of smoothing the image. The smoothed image is defined as/> .

Further, in step two, the inverse transformation is used to reconstruct the details lost during the smoothing process to ensure that important features of the cold reflection signal will not be lost due to processing;

And use information suppression to eliminate information irrelevant to cold reflection in the image to ensure a smoothed image Only critical information is retained.

Further, in step three,

Define the two-dimensional Butterworth function ;

in the formula Indicates the amplitude,/> Indicates attributes,/> Represents the mean value of the attribute,/> Represents the standard deviation of the attribute.

Further, in step four,

Will are initialized to 1; use least squares matrix operation to adjust/> When the fitting accuracy reaches the threshold, the fitted Butterworth fitting parameters are obtained, and the Butterworth fitting parameters are regarded as cold reflection characteristic parameters in the infrared image.

Further, step five specifically includes:

Use the Butterworth function obtained by the least squares method in step 4 as a mathematical model to fit the surface formed after mapping in step 2 to capture and represent the geometric characteristics of the cold reflection signal;

And use the geometric characteristics of the two-dimensional Butterworth function to represent the characteristics of cold reflection in the infrared image: by difference between the fitted Butterworth function and the original infrared image, the noise in the image is removed.

Get the result of step five and extract As an infrared image containing cold reflection noise/> characteristics to achieve the correction of cold reflection noise in infrared images.

A cold reflection noise correction system for infrared images based on Butterworth function fitting:

The correction system includes: an image processing module, a fitting module and a correction module;

The image processing module reads the infrared image ;And design a smooth window for the read infrared image/> Perform smoothing preprocessing; remove excess scene information and inversely transform it to retain the information around the cold reflection to the maximum extent, while suppressing and eliminating information other than it to minimize the image scene information;

The fitting module constructs a two-dimensional Butterworth function, initializes the parameters that need to be fitted; uses the least squares method to fit the data, and finally extracts the parameters of the fitting function as the characteristics of cold reflection in the infrared image;

The correction module uses the two-dimensional Butterworth function to fit the curved surface formed after mapping, uses the geometric characteristics of the two-dimensional Butterworth function to represent the characteristics of cold reflection in the infrared image, and removes the cold reflection in the image by difference with the infrared image. Noise, complete the cold reflection noise correction of infrared images.

An electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the above method are implemented.

A computer-readable storage medium is used to store computer instructions that implement the steps of the above method when executed by a processor.

Beneficial effects of the invention

This invention combines existing infrared noise image denoising methods to analyze infrared images from the perspective of noise distribution. The curved surface formed by mapping the two-dimensional Butterworth function is used to represent the infrared image data characteristics using the geometric characteristics of the two-dimensional Butterworth function.

The method of the present invention performs two-dimensional Butterworth surface fitting on infrared images and performs denoising processing. Compared with other infrared image analysis methods, the present invention can significantly improve the signal-to-noise ratio of the infrared image, thereby removing noise in the image and improving the quality of the infrared image.

Description of the drawings

Figure 1 is a flow chart of the method of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

A method for correcting cold reflection noise in infrared images based on Butterworth function fitting:

Step 1. Read the infrared image through a sensor or other device ;

Step 2. Design a smoothing window for the infrared image read in step 1. Perform smoothing preprocessing; remove redundant scene information and inversely transform it to retain the information around the cold reflection to the maximum extent, while suppressing and eliminating information other than it to minimize the image scene information; making the cold reflection in the original image The reflected signal is more obvious.

In step two, a smoothing window is designed to process local areas of the infrared image to reduce prominent noise points or outliers;

The pretreatment specifically adopts the following formula:

The preprocessing algorithm is: ;

in, Represents image information,/> It represents the maximum gray value of the mean image or The maximum value,/> Represented information pixels/> with cold reflection signal center/> The distance between ,/> It represents the standard deviation in the mean image, which is the degree of suppression of the scene signal in the image;

to infrared images Perform preprocessing to achieve the purpose of smoothing the image. The smoothed image is defined as/> .

In step two, the inverse transformation is used to reconstruct the details lost during the smoothing process to ensure that important features of the cold reflection signal will not be lost due to processing;

And through information suppression (implemented through specific filters or processing techniques) to eliminate information unrelated to cold reflection in the image to ensure a smooth image Only critical information is retained to better highlight cold reflection signals.

Step 3: Construct a two-dimensional Butterworth function and initialize the parameters that need to be fitted;

In step three,

Define the two-dimensional Butterworth function ;

in the formula Indicates the amplitude,/> Indicates attributes,/> Represents the mean value of the attribute,/> Represents the standard deviation of the attribute.

Step 4: Use the least squares method to fit the data, and finally extract the parameters of the fitting function as the cold reflection features in the infrared image to extract the noise features of the infrared image;

In step four, during 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 can better fit the cold reflection signal in the infrared image, with a minimum The square method will try to continuously adjust the parameters of the Butterworth function until it reaches a state that minimizes the error; after the fitting is completed, the parameters of the final Butterworth function will be regarded as a representation of the cold reflection features in the infrared image.

Will are initialized to 1; use least squares matrix operation to adjust/> When the fitting accuracy reaches the threshold, the fitted Butterworth fitting parameters are obtained, and the Butterworth fitting parameters are regarded as cold reflection characteristic parameters in the infrared image.

That is, in step four, through the least squares method, the parameters of the Butterworth function have been adjusted to best fit the cold reflection signal in the original infrared image. This Butterworth function is a mathematical model whose parameters are obtained through a fitting process.

Step 5: Use the two-dimensional Butterworth function to fit the surface formed after mapping, use the geometric characteristics of the two-dimensional Butterworth function to represent the characteristics of cold reflection in the infrared image, and remove the noise in the image by difference with the infrared image. , can clearly distinguish the image from the background, improve the quality of the infrared image, and complete the cold reflection noise correction of the infrared image.

Step five specifically includes:

Use the Butterworth function obtained by the least squares method in step 4 as a mathematical model to fit the surface formed after mapping in step 2 to capture and represent the geometric characteristics of the cold reflection signal;

And use the geometric characteristics of the two-dimensional Butterworth function to represent the characteristics of cold reflection in the infrared image: by difference between the fitted Butterworth function and the original infrared image, the noise in the image is removed.

This is because the fitted function mainly captures the characteristics of the cold reflection signal, while signals unrelated to the cold reflection are removed, thereby improving the quality of the image. Through the above differential operation, noise is removed, making the image clearer. The geometric characteristics of the fitted Butterworth function help distinguish the cold reflection signal from the image background, making the target signal more prominent. Based on the above steps, the overall goal is to improve the quality of infrared images, and at the same time complete the correction of cold reflection noise through fitting Butterworth functions and differential operations.

After completing these steps, the resulting infrared image should be clearer, the cold reflection signal should be more prominent, and the noise should be effectively removed.

Get the result of step five and extract As an infrared image containing cold reflection noise/> characteristics to achieve the correction of cold reflection noise in infrared images.

A cold reflection noise correction system for infrared images based on Butterworth function fitting:

The correction system includes: an image processing module, a fitting module and a correction module;

The image processing module reads infrared images through sensors or other devices ;And design a smooth window for the read infrared image/> Perform smoothing preprocessing; remove redundant scene information and inversely transform it to retain the information around the cold reflection to the maximum extent, while suppressing and eliminating information other than it to minimize the image scene information; making the cold reflection in the original image The reflected signal is more obvious.

The fitting module constructs a two-dimensional Butterworth function, initializes the parameters that need to be fitted; uses the least squares method to fit the data, and finally extracts the parameters of the fitting function as the characteristics of cold reflection in the infrared image to realize the analysis of the infrared image. Extraction of noise features;

The correction module uses the two-dimensional Butterworth function to fit the curved surface formed after mapping, uses the geometric characteristics of the two-dimensional Butterworth function to represent the characteristics of cold reflection in the infrared image, and removes the cold reflection in the image by difference with the infrared image. Noise, it can clearly distinguish the image from the background, improve the quality of the infrared image, and complete the cold reflection noise correction of the infrared image.

An electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the above method are implemented.

A computer-readable storage medium is used to store computer instructions that implement the steps of the above method when executed by a processor.

The memory in the embodiment of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory can be read only memory, ROM, programmable ROM, PROM, erasable programmable read-only memory erasablePROM, EPROM, electrically erasable programmable read-only memory. EPROM, EEPROM or flash memory. Volatile memory can be random access memory, RAM, which is used as an external cache. By way of illustration, but not limitation, many forms of RAM are available, such as static RAM, SRAM, dynamic RAM, DRAM, synchronous DRAM, SDRAM, double data rate synchronous dynamic random access memory double data rate SDRAM, DDR SDRAM, enhanced synchronous dynamic random access memory enhanced SDRAM, ESDRAM, synchlink dynamic random access memory synchlink DRAM, SLDRAM and direct rambus RAM ,DR RAM. It should be noted that the memory of the method described herein is intended to include, but is not 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 using software, it 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 the computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, e.g., the computer instructions may be transferred from a website, computer, server, or data center Transmission to another website, computer, server or data center through wired means such as coaxial cable, optical fiber, digital subscriber line, DSL or wireless means such as infrared, wireless, microwave, etc. 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 one or more available media integrated. The available media may be magnetic media such as floppy disks, hard disks, magnetic tapes, optical media such as high-density digital video discs (DVD), or semiconductor media such as solid state disks (SSD).

During the implementation process, each step of the above method can be completed through the integrated logic circuit of the hardware in the processor or instructions in the form of software. The steps of the methods disclosed in conjunction with the embodiments of the present application can be directly implemented by a hardware processor for execution, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other mature storage media in this field. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

It should be noted that the processor in the embodiment of the present application may be an integrated circuit chip with signal processing capabilities. During the implementation process, each step of the above method embodiment can be completed through an integrated logic circuit of hardware in the processor or instructions in the form of software. The above-mentioned 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 devices, discrete gate or transistor logic devices, or discrete hardware components. Each method, step and logical block diagram disclosed in the embodiment of this application can be implemented or executed. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly implemented by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other mature storage media in this field. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

The cold reflection noise correction method for infrared images based on Butterworth function fitting proposed by the present invention has been introduced in detail above, and the principles and implementations of the present invention have been explained. The description of the above embodiments is only used to help understand the present invention. The method and its core idea; at the same time, for those of ordinary skill in the field, there will be changes in the specific implementation and application scope according to the idea of the present invention. In summary, the contents of this specification should not be understood as Limitations on the invention.

Claims (7)

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 I through a sensor;

step two, designing a smoothing window to carry out smoothing pretreatment on the infrared image I read in the step one; 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;

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 f (x, y) represents image information, max { f } represents the maximum gray value of the mean image or the maximum value of f (x, y), and the distance between the information pixel (x, y) represented by R (x, y) and the cold reflection signal center (a, b), i.e., R (x, y) = | (x-a, y-b) | ₂ Delta represents the standard deviation in the mean image, i.e., the extent to which the scene signal in the image is suppressed;

preprocessing an infrared image I to achieve the purpose of smoothing the image, wherein the smoothed image is defined as I';

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;

the information irrelevant to cold reflection in the image is eliminated through information inhibition, so that only key information of the smoothed image I' is ensured to be reserved;

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 third step of the process, the process is carried out,

definition of two-dimensional Butterworth function

Wherein A represents amplitude, mu ₁ ,μ ₂ Representing the mean, sigma of the attributes _x ,σ _y Representing the standard deviation of the properties.

3. The method according to claim 2, characterized in that: in the fourth step of the process, the process is carried out,

will A, mu ₁ ,μ ₂ ,σ _x ,σ _y All initialized to 1; adjusting A, mu by least square matrix operation ₁ ,μ ₂ ,σ _x ,σ _y And until the fitting precision reaches a threshold value, acquiring the fitted Butterworth fitting parameters, wherein the Butterworth fitting parameters are regarded as cold reflection characteristic parameters in the infrared image.

4. A method according to claim 3, characterized in that:

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 fifth step, extracting (A, mu) ₁ ,μ ₂ ,σ _x ,σ _y ) As a feature of the infrared image I containing the cold reflection noise, correction of the cold reflection noise of the infrared image is realized.

5. 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 image I; a smoothing window is designed to carry out smoothing pretreatment on the read infrared image I; 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;

designing a smooth window to process a local area of the infrared image so as to reduce prominent noise points or abnormal values;

the pretreatment specifically adopts the following formula:

the preprocessing algorithm is as follows:

where f (x, y) represents image information, M represents a maximum gray value of the mean image or a maximum value of f (x, y),the distance between the information pixel (x, y) represented by R (x, y) and the cold reflection signal center (a, b), i.e., R (x, y) = | (x-a, y-b) || ₂ Delta represents the standard deviation in the mean image, i.e., the extent to which the scene signal in the image is suppressed;

preprocessing an infrared image I to achieve the purpose of smoothing the image, wherein the smoothed image is defined as I';

reconstructing lost details in the smoothed process through inverse transformation, so as to ensure that important characteristics of the cold reflection signal are not lost due to processing;

the information irrelevant to cold reflection in the image is eliminated through information inhibition, so that only key information of the smoothed image I' is ensured to be reserved;

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.

6. 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 of claims 1 to 4 when the computer program is executed.

7. 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 4.