A method suitable for removing cold reflection spots

Abstract

A method for removing cold reflection spots. It belongs to the technical field of infrared image spot denoising, and specifically relates to the technical field of infrared image cold reflection noise correction based on Gaussian function combined with fitting. A method for removing cold reflection spots is provided to improve the quality of infrared images. The method comprises the following steps: collecting and identifying infrared images containing cold reflection spots; performing image preprocessing on the identified infrared images; constructing a Gaussian linear filter to filter the preprocessed infrared images to obtain an energy distribution surface of the cold reflection spots; using Gaussian fitting to extract the geometric features of the cold reflection spots and construct a cold reflection spot model; calculating the compensation coefficient; and completing the removal of the cold reflection spots through linear compensation.

Description

Technical Field

[0001] The invention belongs to the technical field of infrared image spot denoising, and in particular to the technical field of infrared image cold reflection noise correction based on Gaussian function combined with fitting. Background Art

[0002] The enhancement and recognition technology of infrared targets has a wide range of application value in the fields of military, security, aerospace, etc. With the development of infrared sensor technology and the expansion of its application fields, infrared imaging systems have become one of the important means of modern monitoring and reconnaissance. However, since infrared imaging systems are easily affected by non-uniform noise in complex environments, the image quality is degraded, which in turn affects the accuracy and reliability of target recognition. Therefore, research based on cold reflection spot removal technology is of great significance to improving the performance of infrared imaging systems.

[0003] The existing method for removing cold reflection spots has very low precision, and the processed infrared image will produce serious artifacts, which has little effect on improving the quality of infrared images. Summary of the invention

[0004] In order to solve the problem that an infrared imaging system is easily affected by non-uniform noise in a complex environment, the present invention provides a method suitable for removing cold reflection spots, thereby improving the quality of infrared images.

[0005] The method comprises the following steps:

[0006] S1, collect and identify infrared images containing cold reflection spots;

[0007] S2, performing image preprocessing on the recognized infrared image;

[0008] S3, constructing a Gaussian linear filter to filter the preprocessed infrared image to obtain an energy distribution surface of the cold reflection spot;

[0009] S4, using Gaussian fitting to extract geometric features of the cold reflection spot and construct a cold reflection spot model;

[0010] S5, calculating the compensation coefficient;

[0011] S6. The cold reflection spot is removed through linear compensation.

[0012] Furthermore, the collecting and identifying the infrared image containing the cold reflection light spot specifically includes: collecting the infrared image containing the cold reflection light spot by means of the cold infrared sensor, and identifying the cold reflection light spot of the infrared image to obtain the identified infrared image.

[0013] Furthermore, the image preprocessing of the recognized infrared image is specifically performed by: using the image preprocessing formula: Perform image preprocessing, where represents the pixel value of the infrared image, Represents the maximum gray value of the infrared image, represents the standard deviation in the mean infrared image, represents a natural constant, Represents the distance from the coordinates of the cold reflection spot area to the coordinates of the center of the cold reflection spot; the image is obtained after preprocessing And the preprocessed infrared image, by analyzing The energy distribution characteristics of the cold reflection spot area are extracted to obtain the coordinate set corresponding to the cold reflection spot area. .

[0014] Furthermore, the filtering preprocessing of the preprocessed infrared image is specifically performed by constructing a Gaussian linear filter: Perform filtering preprocessing, where Indicates The pixel value of the window, Indicates The pixels of the window are smoothed by a moving low-pass filter and the pixel average of the output image is obtained. represents the weight value of Gaussian weighting, Indicates that the infrared image is at position The pixel value of Indicates The pixels in the window are smoothed by a moving low-pass filter and the pixel average of the output image is obtained.

[0015] Furthermore, the Gaussian fitting is specifically: fitting the energy distribution surface and Gaussian function of the cold reflection spot by the least square method; the geometric features include: the center coordinates of the cold reflection spot, the sampling point set, and the diffusion degree and intensity of the cold reflection spot; the cold reflection spot model is: , in, represents the center coordinate of the cold reflection spot, Indicates the total energy of the cold reflection spot.

[0016] Furthermore, the compensation coefficient is calculated by a coefficient fitting method using a cold reflection spot model and a sampling point set.

[0017] Furthermore, the linear compensation is specifically: performing differential extraction on the infrared image containing the cold reflection spot by using the compensation coefficient and the cold reflection spot model.

[0018] The beneficial effects of the method of the present invention are:

[0019] A Gaussian linear filter is used to remove unnecessary scene information around it. The obtained energy distribution surface and Gaussian function are fitted by the least squares method, which can accurately detect the position and size of the light spot in the image. At the same time, a light spot model is established according to the geometric characteristics of the light spot. The coefficient fitting method is used to combine the above-mentioned light spot model and the sampling point set to calculate the compensation coefficient of the fitted image. The compensation coefficient and the light spot model are used to perform linear compensation on the current infrared image, so that the effective information at the corresponding position can be enhanced while eliminating the light spot, and the effective information of the image can be retained as much as possible, which can effectively improve the imaging quality of the infrared camera.

[0020] The present invention can suppress the influence of cold reflection, improve the signal-to-noise ratio of infrared images in a low-temperature environment, thereby removing noise in the image, and can be widely used in industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of the method according to an embodiment of the present invention;

[0022] Figure 2 is a schematic diagram of an infrared image with a cold reflection spot in this embodiment;

[0023] Figure 3 It is a schematic diagram of an infrared image in which cold reflection spots are removed in this embodiment. DETAILED DESCRIPTION

[0024] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of the present invention.

[0025] This embodiment provides a method suitable for removing cold reflection spots. The flowchart of the method is as follows: Figure 1 As shown, the method comprises the following steps:

[0026] S1. Collect and identify infrared images containing cold reflection spots.

[0027] The relevant operations of step S1 are introduced with a specific example:

[0028] like Figure 2 As shown, an infrared image containing a cold reflection light spot is collected by a cold infrared sensor, and the cold reflection light spot of the infrared image is identified to obtain an identified infrared image.

[0029] S2. Perform image preprocessing on the recognized infrared image.

[0030] The relevant operations of step S2 are introduced with a specific example:

[0031] The image preprocessing of the recognized infrared image is specifically performed by: using the image preprocessing formula: Perform image preprocessing, where represents the pixel value of the infrared image, Represents the maximum gray value of the infrared image, Represents the standard deviation in the mean infrared image, that is, the degree of suppression of scene signals in the image. It represents a natural constant, an infinite non-repeating decimal, and a transcendental number, with a value of approximately 2.718281828459045. Represents the distance from the coordinates of the cold reflection spot area to the coordinates of the center of the cold reflection spot; the image is obtained after preprocessing And the preprocessed images, by analyzing The energy distribution characteristics of the cold reflection spot area are extracted to obtain the coordinate set corresponding to the cold reflection spot area. .

[0032] S3. Construct a Gaussian linear filter to filter the preprocessed infrared image to obtain the energy distribution surface of the cold reflection spot.

[0033] The relevant operations of step S3 are introduced with a specific example:

[0034] The spot image after image preprocessing and correction is subjected to Gaussian filtering to filter out unnecessary scene information in the image, smooth the edge line of the spot, and make the energy distribution surface of the cold reflection spot approximate to the Gaussian spot.

[0035] The filtering preprocessing of the preprocessed infrared image is specifically performed by constructing a Gaussian linear filter: Perform filtering preprocessing, where Indicates The pixel value of the window, Indicates The pixels of the window are smoothed by a moving low-pass filter and the pixel average of the output image is obtained. represents the weight value of Gaussian weighting, Indicates that the infrared image is at position The pixel value of Indicates The pixels in the window are smoothed by a moving low-pass filter and the pixel average of the output image is obtained.

[0036] S4. Use Gaussian fitting to extract the geometric features of the cold reflection spot and construct a cold reflection spot model.

[0037] The relevant operations of step S4 are introduced with a specific example:

[0038] The Gaussian fitting is specifically: using the least square method to fit the energy distribution surface and Gaussian function of the cold reflection spot; the geometric features include: the central coordinates of the cold reflection spot, the sampling point set of the cold reflection spot, and the diffusion degree and intensity of the cold reflection spot; the cold reflection spot model is: , in, represents the center coordinate of the cold reflection spot, Indicates the total energy of the cold reflection spot.

[0039] S5. Calculate the compensation coefficient.

[0040] The relevant operations of step S5 are introduced with a specific example:

[0041] The cold reflection spot model and the sampling point set of the cold reflection spot are used to calculate the compensation coefficient by the coefficient fitting method, and the cold reflection spot model is further compensated. The compensation coefficient can be used to quantify the influence of the cold reflection spot on different areas of the image, which is the core parameter for eliminating the spot and enhancing the effective information.

[0042] S6. The cold reflection spot is removed through linear compensation.

[0043] The relevant operations of step S6 are introduced with a specific example:

[0044] The linear compensation is specifically: differential extraction is performed on the infrared image containing the cold reflection spot through the compensation coefficient and the cold reflection spot model, so as to correct the cold reflection spot area. The correction process is specifically as follows: ,in, represents the corrected infrared image, Indicates an infrared image containing cold reflection spots, represents the compensation coefficient; Figure 3 As shown, linear compensation can enhance the effective information at the corresponding position while eliminating the light spot, and retain the effective information of the image as much as possible.

Claims

1. A method for removing cold reflection spots, characterized in that: The method comprises the following steps: S1, collect and identify infrared images containing cold reflection spots; S2, performing image preprocessing on the recognized infrared image; S3, constructing a Gaussian linear filter to filter the preprocessed infrared image to obtain an energy distribution surface of the cold reflection spot; S4, using Gaussian fitting to extract geometric features of the cold reflection spot and construct a cold reflection spot model; The Gaussian fitting is specifically: using the least square method to fit the energy distribution surface and Gaussian function of the cold reflection spot; the geometric features include: the central coordinates of the cold reflection spot, the sampling point set of the cold reflection spot, and the diffusion degree and intensity of the cold reflection spot; the cold reflection spot model is: ,in, represents the center coordinate of the cold reflection spot, Represents the total energy of the cold reflection spot; S5, calculating the compensation coefficient; The compensation coefficient is calculated by a coefficient fitting method using a cold reflection spot model and a sampling point set of the cold reflection spot; S6. The cold reflection spot is removed through linear compensation; The linear compensation specifically includes: performing differential extraction on the infrared image containing the cold reflection spot by using the compensation coefficient and the cold reflection spot model.

2. A method for removing cold reflection spots according to claim 1, characterized in that: The collecting and identifying the infrared image containing the cold reflection light spot specifically includes: collecting the infrared image containing the cold reflection light spot by means of the cold infrared sensor, and identifying the cold reflection light spot of the infrared image to obtain the identified infrared image.

3. A method for removing cold reflection spots according to claim 2, characterized in that: The image preprocessing of the recognized infrared image is specifically performed by: using the image preprocessing formula: Perform image preprocessing, where represents the pixel value of the infrared image, Represents the maximum grayscale value of the infrared image, represents the standard deviation in the mean infrared image, represents a natural constant, Represents the distance from the coordinates of the cold reflection spot area to the coordinates of the center of the cold reflection spot; the image is obtained after preprocessing And the preprocessed infrared image, by analyzing The energy distribution characteristics of the cold reflection spot area are extracted to obtain the coordinate set corresponding to the cold reflection spot area. .

4. A method for removing cold reflection spots according to claim 3, characterized in that: The filtering preprocessing of the preprocessed infrared image is specifically performed by constructing a Gaussian linear filter: Perform filtering preprocessing, where Indicates The pixel value of the window, Indicates The pixels of the window are smoothed by a moving low-pass filter and the pixel average of the output image is obtained. represents the weight value of Gaussian weighting, Indicates that the infrared image is at position The pixel value of Indicates The pixels in the window are smoothed by a moving low-pass filter and the pixel average of the output image is obtained.

Images