Non-uniform noise correction method, electronic device and computer program product

Abstract

The invention provides a non-uniform noise correction method, electronic equipment and a computer program product. In the application, after the output of the infrared imaging equipment at the current temperature is corrected each time, the non-uniform noise fpnM is learned based on the correction result, and the background data corresponding to the highest temperature value and the background data corresponding to the lowest temperature value in the temperature interval of the temperature are adjusted according to the learned non-uniform noise fpnM. According to the mode of updating the dynamically learned non-uniform noise data into the background data in real time, the background data in the stored background-temperature data can be updated in real time based on the non-uniform noise data, and the stored background-temperature data is used for correcting the output of the infrared imaging equipment at a temperature, so that the background data can be updated in real time based on the non-uniform noise data. Therefore, the non-uniform noise correction of the output of the infrared imaging equipment at the temperature is realized equivalently.

Description

Technical Field

[0001] The present application relates to infrared imaging technology, and in particular to a non-uniform noise correction method, an electronic device and a computer program product. Background Art

[0002] The so-called non-uniform noise correction refers to the correction of the non-uniform noise generated by the infrared imaging device in the infrared imaging application. The non-uniform noise here includes the noise introduced by the infrared detection device itself in the infrared imaging device and the noise introduced by the radiation of the structural lens in the infrared imaging device. The infrared imaging device here may include an infrared imaging baffle (referred to as baffle) or may not include a baffle, which is not specifically limited in this application.

[0003] The presence of non-uniform noise will affect the quality of infrared images collected by infrared imaging equipment, and further affect subsequent related image processing based on infrared images, such as target recognition. Summary of the invention

[0004] The present application provides a non-uniform noise correction method, an electronic device and a computer program product to achieve the correction of non-uniform noise.

[0005] The present application provides a non-uniform noise correction method, which is applied to an infrared imaging device. The method includes:

[0006] After correcting the output X of the infrared imaging device at the current temperature T based on the third background data corresponding to the current temperature T, learning the non-uniform noise fpn_M based on the correction result; wherein the third background data corresponding to the current temperature T is determined based on the fitting of the first background data and the second background data; the first background data and the second background data are respectively the local data corresponding to the highest temperature value and the temperature data corresponding to the lowest temperature value in the temperature interval where the current temperature T is located in the stored background-temperature data; the temperature interval is determined based on the temperature interval between each adjacent temperature in the stored background-temperature data;

[0007] The first background data and the second background data in the background-temperature data are updated respectively based on the non-uniform noise fpn_M.

[0008] The present application provides a non-uniform noise correction method, which is applied to an infrared imaging device. The method includes:

[0009] The third background data corresponding to the current temperature T is determined based on the first background data, the second background data and the learned non-uniform noise fpn_M fitting; the first background data and the second background data are respectively the local data corresponding to the highest temperature value and the temperature data corresponding to the lowest temperature value in the temperature interval where the current temperature T is located in the stored background-temperature data; the temperature interval is determined based on the temperature interval between each adjacent temperature in the stored background-temperature data;

[0010] The output X of the infrared imaging device at the current temperature T is corrected based on the third background data.

[0011] The present application provides a non-uniform noise correction method, which is applied to an infrared imaging device. The method includes:

[0012] The third background data corresponding to the current temperature T is determined based on the first background data and the second background data; the first background data and the second background data are respectively the local data corresponding to the highest temperature value and the temperature data corresponding to the lowest temperature value in the temperature interval where the current temperature T is located in the stored background-temperature data; the temperature interval is determined based on the temperature interval between each adjacent temperature in the stored background-temperature data;

[0013] The output X of the infrared imaging device at the current temperature T is corrected based on the third background data and the learned non-uniform noise fpn_M.

[0014] The embodiment of the present application also provides an electronic device. The electronic device includes: a processor and a machine-readable storage medium;

[0015] The machine-readable storage medium stores machine-executable instructions that can be executed by the processor;

[0016] The processor is used to execute machine executable instructions to implement the steps of the above disclosed method.

[0017] An embodiment of the present application further provides a computer program product, wherein the computer program product stores a computer program, and when the computer program is executed by a processor, the steps of the above-disclosed method are implemented.

[0018] It can be seen from the above technical scheme that in the present application, each time this embodiment corrects the output of the infrared imaging device at the current temperature, it will learn the non-uniform noise fpn_M based on the correction result, and adjust the background data corresponding to the highest temperature value and the background data corresponding to the lowest temperature value in the temperature range where the temperature is located according to the learned non-uniform noise fpn_M. This method of updating the dynamically learned non-uniform noise data to the background data in real time can realize the real-time update of the background data in the stored background-temperature data based on the non-uniform noise data, and since the stored background-temperature data is used to correct the output of the infrared imaging device at a certain temperature, this is equivalent to realizing the non-uniform noise correction of the output of the infrared imaging device at a certain temperature.

[0019] Furthermore, in the present embodiment, the dynamically learned non-uniform noise data is updated in real time to the background data. Thus, even if the infrared imaging device switches from motion to still state or the infrared imaging device is cold-started statically, the output of the infrared imaging device can still be corrected using the background data updated based on the non-uniform noise data, thereby ensuring the image effect of the entire motion and still process, avoiding the problem of poor image effect when the device is just started, and avoiding the defect of incompatibility of pre-stored background data after the infrared imaging device has been aged at high and low temperatures.

[0020] Furthermore, in this embodiment, the non-uniform noise fpn_M is taken into consideration each time when fitting the third background data corresponding to the current temperature T, and the non-uniform noise fpn_M is involved in determining the third background data corresponding to the current temperature T, so as to ensure that the third background data finally determined is corrected by the non-uniform noise fpn_M, and then the output X of the infrared imaging device at the current temperature T is corrected by the third background data corrected by the non-uniform noise fpn_M, which is equivalent to achieving non-uniform noise correction for the output X of the infrared imaging device at the current temperature T.

[0021] Furthermore, in this embodiment, the learned non-uniform noise fpn_M is directly used to correct the output X of the infrared imaging device at the current temperature T, which realizes the correction of the non-uniform noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0023] Figure 1 A flow chart of the method provided in the embodiment of the present application;

[0024] Figure 2 Another method flow chart provided for an embodiment of the present application;

[0025] Figure 3 Another method flow chart provided for an embodiment of the present application;

[0026] Figure 4a to Figure 4b A comparison diagram of the effects provided by the embodiments of the present application;

[0027] Figure 5a to Figure 5b Another effect comparison diagram provided for the embodiment of the present application;

[0028] Figure 6 A structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the technical solutions provided by the embodiments of the present application and to make the above-mentioned purposes, features and advantages of the embodiments of the present application more obvious and understandable, the technical solutions in the embodiments of the present application are further described in detail below in conjunction with the accompanying drawings.

[0030] The following describes how to obtain background-temperature data:

[0031] As an embodiment, the infrared imaging device can be placed in a high and low temperature box in advance and the structural lens of the infrared imaging device can be covered with black foam. The infrared imaging device can be triggered to collect infrared images at certain temperature intervals, such as every 5 degrees Celsius, to obtain infrared images at various temperatures (also called basic background data). For example, the background data Base1 under T1, the background data Base2 under T2, and so on, will eventually obtain a series of background-temperature data: [Base1 Base2...BaseN] and [T1 T2...TN].

[0032] It should be noted that, in order to reduce the influence of random noise, the background data at any of the above temperatures can be determined by multi-frame calculations, such as averaging. For example, taking the background data Base1 at temperature T1 as an example, the infrared images collected by the infrared imaging device at temperature T1 can be averaged (for example, the grayscale values ​​of the pixels at the same position in each infrared image are averaged), and the result is used as the background data Base1 at temperature T1.

[0033] Initially, the above series of background-temperature data are stored in an infrared imaging device, such as a core of the infrared imaging device.

[0034] Based on the above description, the method provided in the embodiment of the present application is described below:

[0035] See also Figure 1 , Figure 1 The first method flow chart provided in the embodiment of the present application is applied to infrared imaging equipment, such as Figure 1 As shown, the method may include the following steps:

[0036] Step 101 : after correcting the output X of the infrared imaging device at the current temperature T based on the third background data corresponding to the current temperature T, learn the non-uniform noise fpn_M based on the correction result.

[0037] In this embodiment, before executing step 101, the first background data corresponding to the highest temperature value and the second background data corresponding to the lowest temperature value in the temperature interval where the current temperature T is located can be determined from the stored background-temperature data. The background-temperature data described above, based on the temperature interval between each adjacent temperature in the background-temperature data, such as 5 degrees Celsius, has implied various temperature intervals, such as 0-5 degrees Celsius, 5-10 degrees Celsius, and so on. Based on this, this embodiment can easily determine the temperature interval where the current temperature T is located in the temperature interval implied by the above background-temperature data. Afterwards, the first background data corresponding to the highest temperature value and the second background data corresponding to the lowest temperature value can be found from the stored background-temperature data.

[0038] As an embodiment, the first background data and the second background data are the background data initially acquired as mentioned above, and may also be background data updated in a manner similar to the following step 102, which is not specifically limited in this embodiment.

[0039] After obtaining the first background data corresponding to the highest temperature value and the second background data corresponding to the lowest temperature value, a fitting method can be used to determine the third background data corresponding to the current temperature T. There are many fitting methods here, such as linear fitting, nonlinear fitting, and the like.

[0040] Taking linear fitting as an example, assuming that the first background data corresponding to the above-mentioned highest temperature value (denoted as T_H) is B_H, and the second background data corresponding to the lowest temperature value (denoted as T_L) is B_L, then the third background data corresponding to the current temperature T (denoted as B_ins) can be determined in the following way: B_ins=B_H*(T-T_L) / (T_H-T_L)+B_L*(T_H-T) / (T_H-T_L).

[0041] As for how to correct the output X of the infrared imaging device at the current temperature T based on the third background data corresponding to the current temperature T, there are many ways to implement it, such as correcting it according to the following formula: Y=K*(X-B_ins)+Offset. Among them, Y represents the correction result, K and Offset are the set gain coefficient and the set offset respectively. B_ins is described above. X represents the output of the infrared imaging device at the current temperature T. Optionally, X-B_ins can represent the difference calculation of the grayscale values ​​of pixels at the same position of X and B_ins.

[0042] In this embodiment, the non-uniform noise fpn_M can be extracted by a scene-based online learning solution, or by time domain filtering, constant statistics, registration, neural network, etc., which is not specifically limited in this embodiment. However, the overall idea is motion judgment, noise learning extraction, accumulation to obtain non-uniform noise fpn_M, etc. It should be noted that the non-uniform noise learned at different times may be different, which is not specifically limited in this embodiment.

[0043] Step 102: Update the first background data and the second background data in the background-temperature data respectively based on the non-uniform noise fpn_M.

[0044] As an embodiment, updating the first background data in the background-temperature data based on the non-uniform noise fpn_M includes: substituting the first background data, the current temperature T, the maximum temperature value, and the minimum temperature value into a first specified algorithm to obtain the updated first background data. For example, the first background data is updated according to the following formula: B_L ‘ =B_L+fpn_M*(T-T_L) / (T_H-T_L); among them, B_L ‘ represents the updated first background data, T_L represents the above-mentioned minimum temperature value, T_H represents the above-mentioned maximum temperature value, and B_L represents the above-mentioned first background data.

[0045] As an embodiment, updating the second background data in the background-temperature data based on the non-uniform noise fpn_M includes: substituting the second background data, the current temperature T, the above-mentioned maximum temperature value, and the above-mentioned minimum temperature value into the second specified algorithm to obtain the updated second background data. For example, the second background data is updated according to the following formula: B_H ‘ =B_H+fpn_M*(T_H-T) / (T_H-T_L). Among them, B_H ‘ represents the updated second background data, T_L represents the above-mentioned minimum temperature value, T_H represents the above-mentioned maximum temperature value, and B_H represents the above-mentioned second background data.

[0046] It can be found that each time after the output of the infrared imaging device at the current temperature is corrected, the present embodiment will learn the non-uniform noise fpn_M based on the correction result, and adjust the background data corresponding to the highest temperature value and the background data corresponding to the lowest temperature value in the temperature range where the temperature is located according to the learned non-uniform noise fpn_M. This method of updating the dynamically learned non-uniform noise data to the background data in real time can be used in static scenes, especially in application scenarios when the infrared imaging device is statically turned on, to directly use the background data updated based on the non-uniform noise data to correct the output of the infrared imaging device, thereby avoiding the problem of poor image effect when the device is just turned on, and also avoiding the defect of incompatibility of the pre-stored background data of the infrared imaging device after high and low temperature aging.

[0047] In addition, this method of updating the dynamically learned non-uniform noise data to the background data in real time means that the stored background data is updated in real time based on the non-uniform noise, and also means that the output of the infrared imaging device at a certain temperature is subsequently corrected based on the updated background data, so that each subsequent non-uniform noise is learned based on the correction result, which can speed up the convergence of the non-uniform noise.

[0048] So far, completed Figure 1 Description of the process shown.

[0049] Another method provided in the embodiment of the present application is described below:

[0050] See also Figure 2 , Figure 2 Another method flow chart provided in the embodiment of the present application. The method is applied to infrared imaging equipment. Figure 2 As shown, the method comprises the following steps:

[0051] Step 201, based on the first background data, the second background data and the learned non-uniform noise fpn_M fitting, the third background data corresponding to the current temperature T is determined, the first background data and the second background data are respectively the local data corresponding to the highest temperature value and the temperature data corresponding to the lowest temperature value in the temperature interval where the current temperature T is located in the stored background-temperature data; the temperature interval is determined based on the temperature interval between each adjacent temperature in the stored background-temperature data.

[0052] For example, in this embodiment, the first background data corresponding to the highest temperature value and the second background data corresponding to the lowest temperature value in the temperature interval where the current temperature T is located will be determined from the stored background-temperature data. The background-temperature data described above, based on the temperature interval between each adjacent temperature in the background-temperature data, such as 5 degrees Celsius, has implied various temperature intervals, such as 0-5 degrees Celsius, 5-10 degrees Celsius, and so on. Based on this, the temperature interval where the current temperature T is located can be easily determined in this embodiment. Afterwards, the first background data corresponding to the highest temperature value and the second background data corresponding to the lowest temperature value can be found from the stored background-temperature data.

[0053] After that, the third background data corresponding to the current temperature T can be determined based on the first background data, the second background data and the learned non-uniform noise fpn_M. For example, based on the above-mentioned maximum temperature value and the above-mentioned minimum temperature value, the first background data and the second background data are fitted to obtain a fitting result; there are many ways to fit, such as linear fitting, nonlinear fitting, etc. Taking linear fitting as an example, assuming that the first background data corresponding to the above-mentioned maximum temperature value (denoted as T_H) is B_H, and the second background data corresponding to the minimum temperature value (denoted as T_L) is B_L, then the fitting result can be determined in the following way: B_ins ’ =B_H*(T-T_L) / (T_H-T_L)+B_L*(T_H-T) / (T_H-T_L). Then, the third background data is determined based on the difference between the fitting result and the non-uniform noise fpn_M. For example, the third background data corresponding to the current temperature T (referred to as B_ins) can be: B_ins=B_ins ’ -fpn_M.

[0054] It should be noted that the non-uniform noise fpn_M here may be the non-uniform noise learned from the previous calibration result. The learning method is as described above, and this embodiment does not specifically limit it.

[0055] Step 202: Correct the output X of the infrared imaging device at the current temperature T based on the third background data.

[0056] As for how to correct the output X of the infrared imaging device at the current temperature T based on the third background data corresponding to the current temperature T, there are many ways to implement it, such as correcting it according to the following formula: Y=K*(X-B_ins)+Offset. Among them, Y represents the correction result, K and Offset are the set gain coefficient and the set offset respectively. B_ins is described above. X represents the output of the infrared imaging device at the current temperature T. Optionally, X-B_ins can represent the difference calculation of the grayscale values ​​of pixels at the same position of X and B_ins.

[0057] So far, completed Figure 2 The process shown.

[0058] pass Figure 2 It can be seen from the process shown that in this embodiment, the non-uniform noise fpn_M is taken into consideration each time when fitting the third background data corresponding to the current temperature T, and the non-uniform noise fpn_M is involved in determining the third background data corresponding to the current temperature T, so as to ensure that the third background data finally determined is corrected by the non-uniform noise fpn_M, and then the output X of the infrared imaging device at the current temperature T is corrected by the third background data corrected by the non-uniform noise fpn_M, which is equivalent to achieving the non-uniform noise correction of the output X of the infrared imaging device at the current temperature T.

[0059] Another method provided in the embodiment of the present application is described below:

[0060] See also Figure 3 , Figure 3 Another method flow chart provided in the embodiment of the present application. The method is applied to infrared imaging equipment. Figure 3 As shown, the process may include the following steps:

[0061] Step 301, determine the third background data corresponding to the current temperature T based on the fitting of the first background data and the second background data; the first background data and the second background data are respectively the local data corresponding to the highest temperature value and the temperature data corresponding to the lowest temperature value in the temperature interval where the current temperature T is located in the stored background-temperature data; the temperature interval is determined based on the temperature interval between each adjacent temperature in the stored background-temperature data.

[0062] For example, in this embodiment, the first background data corresponding to the highest temperature value and the second background data corresponding to the lowest temperature value in the temperature interval where the current temperature T is located will be determined from the stored background-temperature data. The background-temperature data described above, based on the temperature interval between each adjacent temperature in the background-temperature data, such as 5 degrees Celsius, has implied various temperature intervals, such as 0-5 degrees Celsius, 5-10 degrees Celsius, and so on. Based on this, the temperature interval where the current temperature T is located can be easily determined in this embodiment. Afterwards, the first background data corresponding to the highest temperature value and the second background data corresponding to the lowest temperature value can be found from the stored background-temperature data.

[0063] After that, the third background data corresponding to the current temperature T can be determined based on the first background data and the second background data. For example, based on the above-mentioned maximum temperature value and the above-mentioned minimum temperature value, the first background data and the second background data are fitted to obtain the third background data corresponding to the current temperature T. There are many ways of fitting, such as linear fitting, nonlinear fitting, etc. Taking linear fitting as an example, assuming that the first background data corresponding to the above-mentioned maximum temperature value (denoted as T_H) is B_H and the second background data corresponding to the minimum temperature value (denoted as T_L) is B_L, then the third background data corresponding to the current temperature T (denoted as B_ins) can be determined in the following way: B_ins=B_H*(T-T_L) / (T_H-T_L)+B_L*(T_H-T) / (T_H-T_L).

[0064] Step 302: Correct the output X of the infrared imaging device at the current temperature T based on the third background data and the learned non-uniform noise fpn_M.

[0065] In this embodiment, based on the third background data and the learned non-uniform noise fpn_M, correcting the output X of the infrared imaging device at the current temperature T may include: substituting the above X, the third background data, the set gain coefficient K and the set offset Offset into the third specified algorithm to obtain the preliminary correction result of X (represented as Y ‘ ), such as Y ‘ =K*(X-B_ins)+Offset; then, the preliminary correction result is corrected based on the learned non-uniform noise fpn_M to obtain a target correction result. For example, the target correction result (denoted as Y) can be expressed by the following formula: Y=K*(X-B_ins)-fpn_M+Offset.

[0066] It can be seen that in this embodiment, the output X of the infrared imaging device at the current temperature T is corrected directly using the learned non-uniform noise fpn_M, which realizes the correction of non-uniform noise. It should be noted that the non-uniform noise fpn_M here can be the non-uniform noise learned from the previous correction result. The learning method is as described above, and this embodiment is not specifically limited.

[0067] So far, completed Figure 3 The process shown.

[0068] Figure 4a to Figure 4b A schematic diagram comparing the effects before and after correction using any of the above methods is shown. Figure 5a to Figure 5bA schematic diagram comparing the effects before and after correction according to any of the above methods is shown. It can be seen that by correcting according to any of the above methods, the quality of the infrared images collected by the infrared imaging device can be greatly improved, thereby improving the accuracy of subsequent related image processing based on the infrared image, such as target recognition.

[0069] This application scheme is particularly suitable for thermal imaging devices without baffles. The baffle is a structure used by traditional thermal imaging cores to obtain noise correction data. During the imaging process of the core, it needs to be triggered regularly, which will cause the scene to freeze. Without baffles, that is, removing the baffle structure, the core structure size and power consumption can be made smaller, and there is no problem of imaging freezing.

[0070] The method provided in the embodiment of the present application is described above. The device provided in the embodiment of the present application is described below:

[0071] See also Figure 6 , Figure 6 This is a structural diagram of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the hardware structure may include: a processor and a machine-readable storage medium, the machine-readable storage medium storing machine-executable instructions that can be executed by the processor; the processor is used to execute the machine-executable instructions to implement the method disclosed in the above example of this application.

[0072] Based on the same application concept as the above method, an embodiment of the present application also provides a machine-readable storage medium, on which a number of computer instructions are stored. When the computer instructions are executed by a processor, the method disclosed in the above example of the present application can be implemented.

[0073] Based on the same application concept as the above method, an embodiment of the present application further provides a computer program product storing a computer program, and when the computer program is executed by a processor, the method disclosed in the above example of the present application is implemented.

[0074] Exemplarily, the above-mentioned machine-readable storage medium can be any electronic, magnetic, optical or other physical storage device that can contain or store information, such as executable instructions, data, etc. For example, the machine-readable storage medium can be: RAM (Radom Access Memory), volatile memory, non-volatile memory, flash memory, storage drive (such as hard disk drive), solid state drive, any type of storage disk (such as CD, DVD, etc.), or similar storage medium, or a combination thereof.

[0075] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A non-uniform noise correction method, characterized in that: The method is applied to infrared imaging equipment, and the method comprises: After correcting the output X of the infrared imaging device at the current temperature T based on the third background data corresponding to the current temperature T, learning the non-uniform noise fpn_M based on the correction result; wherein the third background data corresponding to the current temperature T is determined based on the fitting of the first background data and the second background data; the first background data and the second background data are respectively the local data corresponding to the highest temperature value and the temperature data corresponding to the lowest temperature value in the temperature interval where the current temperature T is located in the stored background-temperature data; the temperature interval is determined based on the temperature interval between each adjacent temperature in the stored background-temperature data; The first background data and the second background data in the background-temperature data are updated respectively based on the non-uniform noise fpn_M.

2. The method according to claim 1, characterized in that The updating of the first background data in the background-temperature data based on the non-uniform noise fpn_M includes: substituting the first background data, the current temperature T, the maximum temperature value, and the minimum temperature value into a first specified algorithm to obtain updated first background data; The updating of the second background data in the background-temperature data based on the non-uniform noise fpn_M includes: substituting the second background data, the current temperature T, the maximum temperature value, and the minimum temperature value into a second specified algorithm to obtain updated second background data.

3. The method according to claim 2, characterized in that The updating of the first background data based on the non-uniform noise fpn_M includes: Update the first background data according to the following formula: B_L ‘ =B_L+fpn_M*(T-T_L) / (T_H-T_L); Among them, B_L ‘ represents the updated first background data, T_L represents the lowest temperature value, T_H represents the highest temperature value, and B_L represents the first background data.

4. The method according to claim 2, characterized in that: The updating of the second background data based on the non-uniform noise fpn_M includes: Update the second background data according to the following formula: B_H ‘ =B_H+fpn_M*(T_H-T) / (T_H-T_L); Among them, B_H ‘ represents the updated second background data, T_L represents the lowest temperature value, T_H represents the highest temperature value, and B_H represents the second background data.

5. A method for correcting non-uniform noise, characterized in that: The method is applied to infrared imaging equipment, and the method comprises: The third background data corresponding to the current temperature T is determined based on the first background data, the second background data and the learned non-uniform noise fpn_M fitting; the first background data and the second background data are respectively the local data corresponding to the highest temperature value and the temperature data corresponding to the lowest temperature value in the temperature interval where the current temperature T is located in the stored background-temperature data; the temperature interval is determined based on the temperature interval between each adjacent temperature in the stored background-temperature data; The output X of the infrared imaging device at the current temperature T is corrected based on the third background data.

6. The method according to claim 1, characterized in that The method of determining the third background data corresponding to the current temperature T based on the first background data, the second background data and the learned non-uniform noise fpn_M fitting includes: Based on the maximum temperature value and the minimum temperature value, fitting the first background data and the second background data to obtain a fitting result; The third background data is determined based on a difference between the fitting result and the non-uniform noise fpn_M.

7. A method for correcting non-uniform noise, characterized in that: The method is applied to infrared imaging equipment, and the method comprises: The third background data corresponding to the current temperature T is determined based on the first background data and the second background data; the first background data and the second background data are respectively the local data corresponding to the highest temperature value and the temperature data corresponding to the lowest temperature value in the temperature interval where the current temperature T is located in the stored background-temperature data; the temperature interval is determined based on the temperature interval between each adjacent temperature in the stored background-temperature data; The output X of the infrared imaging device at the current temperature T is corrected based on the third background data and the learned non-uniform noise fpn_M.

8. The method according to claim 7, characterized in that The correcting the output X of the infrared imaging device at the current temperature T based on the third background data and the learned non-uniform noise fpn_M includes: Substituting the X, the third background data, the set gain coefficient K and the set offset Offset into a third specified algorithm to obtain a preliminary correction result of the X; The preliminary correction result is corrected based on the learned non-uniform noise fpn_M to obtain a target correction result.

9. An electronic device, characterized in that: The electronic device includes: a processor and a machine-readable storage medium; The machine-readable storage medium stores machine-executable instructions that can be executed by the processor; The processor is used to execute machine executable instructions to implement the method steps of any one of claims 1-8.

10. A computer program product, characterized in that The computer program product stores a computer program, and when the computer program is executed by a processor, the method of any one of claims 1 to 8 is implemented.

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