Non-uniform noise correction method, electronic device, and computer program product
By dynamically learning the non-uniform noise fpn_M and updating the baseline data of the infrared imaging device in real time, the image quality problem caused by non-uniform noise in the infrared imaging device is solved, ensuring the image acquisition and processing effect of the device under different conditions, especially the stability during power-on and temperature changes.
Patent Information
- Application Number
- CN202411622220.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-13
- Publication Date
- 2026-08-25
- Estimated Expiration
- 2044-11-13
AI Technical Summary
Non-uniform noise in infrared imaging equipment affects image quality and leads to poor subsequent image processing results, especially when the equipment is turned on and when the temperature changes.
By dynamically learning the non-uniform noise fpn_M, the baseline data of the infrared imaging device is updated in real time. The learned non-uniform noise fpn_M is used to correct the device output, including online learning and fitting techniques, to ensure the accuracy of the correction results.
It achieves stable image quality under both moving and stationary conditions, avoiding problems such as poor image quality at startup and incompatibility of baseline data after aging at high and low temperatures, thus improving the accuracy of image acquisition and processing.
Smart Images
Figure CN119533680B_ABST
Abstract
Description
Technical Field
[0001] This application relates to infrared imaging technology, and in particular to non-uniform noise correction methods, electronic devices, and computer program products. Background Technology
[0002] Non-uniform noise correction refers to the correction of non-uniform noise generated by infrared imaging equipment in infrared image applications. This non-uniform noise includes noise introduced by the infrared detector itself within the infrared imaging equipment, as well as noise introduced by radiation from structural elements such as lenses within the infrared imaging equipment. The infrared imaging equipment may or may not include an infrared imaging shield (hereinafter referred to as a shield); this application does not specifically limit its scope.
[0003] The presence of non-uniform noise can affect the quality of infrared images acquired by infrared imaging equipment, which in turn can affect subsequent image processing based on infrared images, such as target recognition. Summary of the Invention
[0004] This application provides a non-uniform noise correction method, electronic equipment, and computer program product for achieving non-uniform noise correction.
[0005] This 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, non-uniform noise fpn_M is learned based on the correction result; wherein, the third background data corresponding to the current temperature T is determined by fitting 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 lowest temperature value in the temperature range where the current temperature T is located from the stored background-temperature data; the temperature range 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 based on the non-uniform noise fpn_M.
[0008] This 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 lowest temperature value in the temperature range where the current temperature T is located from the stored background-temperature data; the temperature range 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] This 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 by fitting 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 lowest temperature value in the temperature range where the current temperature T is located from the stored background-temperature data; the temperature range is determined based on the temperature interval between each adjacent temperature in the stored background-temperature data.
[0013] Based on the third background data and the learned non-uniform noise fpn_M, the output X of the infrared imaging device at the current temperature T is corrected.
[0014] This 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 disclosed method.
[0017] This application also provides a computer program product, which stores a computer program that, when executed by a processor, implements the steps of the method disclosed above.
[0018] As can be seen from the above technical solutions, in this application, after each correction of the output of the infrared imaging device at the current temperature, the non-uniform noise fpn_M is learned based on the correction result, and the background data corresponding to the highest temperature value and the lowest temperature value in the temperature range are adjusted according to the learned non-uniform noise fpn_M. This method of updating the background data in real time with the dynamically learned non-uniform noise data can realize the real-time update of the background data in the stored background-temperature data based on the non-uniform noise data. 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 this embodiment, the method of updating the background data with the non-uniform noise data learned in real time ensures that even if the infrared imaging device switches from a moving state to a stationary state or is turned on statically, the output of the infrared imaging device can still be corrected using the background data updated based on the non-uniform noise data. This ensures the image quality throughout the moving and stationary processes, avoids the problem of poor image quality when the device is first turned on, and avoids the defect of incompatibility of the pre-stored background data after the infrared imaging device has undergone high and low temperature aging.
[0020] Furthermore, in this embodiment, when fitting the third background data corresponding to the current temperature T each time, non-uniform noise fpn_M is taken into account. Non-uniform noise fpn_M is included in determining the third background data corresponding to the current temperature T, ensuring that the finally determined third background data is corrected by non-uniform noise fpn_M. Then, the third background data corrected by non-uniform noise fpn_M is used to correct the output X of the infrared imaging device at the current temperature T, which is equivalent to realizing non-uniform noise correction of 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 achieves the correction of non-uniform noise. Attached Figure Description
[0022] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0023] Figure 1 A flowchart illustrating the method provided in this application embodiment;
[0024] Figure 2 Another method flowchart provided for embodiments of this application;
[0025] Figure 3 Another method flowchart provided for embodiments of this application;
[0026] Figures 4a to 4b Comparison diagrams of effects provided for embodiments of this application;
[0027] Figures 5a to 5b Another effect comparison diagram provided for the embodiments of this application;
[0028] Figure 6 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation
[0029] To enable those skilled in the art to better understand the technical solutions provided in the embodiments of this application, and to make the above-mentioned objectives, features and advantages of the embodiments of this application more apparent and understandable, the technical solutions in the embodiments of this application will be further described in detail below with reference to the accompanying drawings.
[0030] The following describes how to obtain background temperature data:
[0031] As an example, the infrared imaging device can be placed in a high-low temperature chamber beforehand and its lens can be covered with black foam. The infrared imaging device is then triggered to acquire infrared images at certain temperature intervals, such as every 5 degrees Celsius, to obtain infrared images (also known as baseline data) at each temperature. For example, the baseline data Base1 is obtained at T1, the baseline data Base2 is obtained at T2, and so on, eventually resulting in a series of baseline-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 calculation, such as by averaging. For example, taking the background data Base1 at temperature T1 as an example, the infrared images acquired by the infrared imaging device at temperature T1 can be averaged (for example, the gray values of pixels at the same position in each infrared image can be averaged), and the result can be used as the background data Base1 at temperature T1.
[0033] Initially, the aforementioned series of background temperature data will be stored in the core of an infrared imaging device, such as an infrared imaging device.
[0034] Based on the above description, the method provided in the embodiments of this application will be described below:
[0035] See Figure 1 , Figure 1 A flowchart of a first method provided for an embodiment of this application. This method is applied to an infrared imaging device, 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 range of the current temperature T can be determined from the stored background-temperature data. As described above, the background-temperature data implicitly includes temperature ranges, such as 0-5 degrees Celsius, 5-10 degrees Celsius, and so on, based on the temperature intervals between adjacent temperatures implied by the background-temperature data. Therefore, this embodiment can easily determine the temperature range of the current temperature T from the temperature ranges implied by the background-temperature data. Then, 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 example, the first baseline data and the second baseline data are the baseline data initially obtained above, or they can be baseline data updated in a manner similar to step 102 below. This example is not specifically limited.
[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, such as linear fitting and nonlinear fitting.
[0040] Taking linear fitting as an example, assuming that the first background data corresponding to the 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 as follows: B_ins=B_H*(T-T_L) / (T_H-T_L)+B_L*(T_H-T) / (T_H-T_L).
[0041] Regarding 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. For example, it can be corrected according to the following formula: Y = K * (X - B_ins) + Offset. Where Y represents the correction result, K and Offset are the set gain coefficient and set offset, respectively. B_ins is as described above. X represents the output of the infrared imaging device at the current temperature T. Optionally, X - B_ins can represent the difference in grayscale values between pixels at the same position in X and B_ins.
[0042] In this embodiment, the non-uniform noise fpn_M can be extracted using a scene-based online learning scheme, or it can be extracted using methods such as temporal filtering, constant statistics, registration, neural networks, etc. This embodiment is not specifically limited. However, the overall idea is the same: motion judgment, noise learning and extraction, and accumulation to obtain the non-uniform noise fpn_M. It should be noted that the non-uniform noise may be different in different learning iterations, and this embodiment is not specifically limited.
[0043] Step 102: Update the first and second background data in the background-temperature data based on the non-uniform noise fpn_M.
[0044] As an example, updating the first background data in the background-temperature data based on non-uniform noise fpn_M includes: substituting the first background data, the current temperature T, the highest temperature value, and the lowest 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 ‘ This represents the updated first background data, where T_L represents the lowest temperature value, T_H represents the highest temperature value, and B_L represents the first background data.
[0045] As an example, updating the second background data in the background-temperature data based on non-uniform noise fpn_M includes: substituting the second background data, the current temperature T, the aforementioned highest temperature value, and the aforementioned lowest temperature value into a 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 ‘ This represents the updated second background data, where T_L represents the lowest temperature value, T_H represents the highest temperature value, and B_H represents the second background data.
[0046] It can be observed that in this embodiment, after each correction of the output of the infrared imaging device at the current temperature, the non-uniform noise fpn_M is learned based on the correction result. The background data corresponding to the highest temperature value and the background data corresponding to the lowest temperature value in the temperature range are adjusted according to the learned non-uniform noise fpn_M. This method of updating the background data with dynamically learned non-uniform noise data in real time can directly use the background data updated based on non-uniform noise data to correct the output of the infrared imaging device in static scenarios, especially in application scenarios where the infrared imaging device is statically powered on. This avoids the problem of poor image quality when the device is first powered on, and also avoids the defect of incompatibility of the pre-stored background data after the infrared imaging device has undergone high and low temperature aging.
[0047] Furthermore, this method of updating the non-uniform noise data learned dynamically into the background data in real time means that the stored background data is updated in real time based on the non-uniform noise. It also means that the output of the infrared imaging device at a certain temperature is subsequently corrected based on the updated background data. This allows the method of learning non-uniform noise based on the correction results to accelerate the convergence speed of non-uniform noise.
[0048] This concludes the process. Figure 1 Description of the process shown.
[0049] Another method provided by an embodiment of this application is described below:
[0050] See Figure 2 , Figure 2 This is another method flowchart provided for an embodiment of this application. The method is applied to an infrared imaging device. Figure 2 As shown, the method includes the following steps:
[0051] Step 201: Based on the first background data, the second background data, and the learned non-uniform noise fpn_M, determine the third background data corresponding to the current temperature T. The first background data and the second background data are the local data corresponding to the highest temperature value and the lowest temperature value in the temperature range where the current temperature T is located, respectively, from the stored background-temperature data. The temperature range 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 within the temperature range of the current temperature T are first determined from the stored background-temperature data. As described above, the background-temperature data, based on the temperature interval between adjacent temperatures (e.g., 5 degrees Celsius), implicitly contains temperature ranges, such as 0-5 degrees Celsius, 5-10 degrees Celsius, and so on. Based on this, this embodiment can easily determine the temperature range of the current temperature T. Then, 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] Then, the third background data corresponding to the current temperature T can be determined by fitting the first background data, the second background data, and the learned non-uniform noise fpn_M. For example, based on the above highest temperature value and the above lowest temperature value, the first background data and the second background data are fitted to obtain the fitting result; there are many ways to do this fitting, such as linear fitting and nonlinear fitting. Taking linear fitting as an example, assuming that the first background data corresponding to the above 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, the fitting result can be determined as follows: B_ins ’ =B_H*(T-T_L) / (T_H-T_L)+B_L*(T_H-T) / (T_H-T_L). Then, based on the difference between the fitting result and the non-uniform noise fpn_M, the third background data is determined. For example, the third background data (denoted as B_ins) corresponding to the current temperature T can be: B_ins = B_ins ’ -fpn_M.
[0054] 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.
[0055] Step 202: Correct the output X of the infrared imaging device at the current temperature T based on the third background data.
[0056] Regarding 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. For example, it can be corrected according to the following formula: Y = K * (X - B_ins) + Offset. Where Y represents the correction result, K and Offset are the set gain coefficient and set offset, respectively. B_ins is as described above. X represents the output of the infrared imaging device at the current temperature T. Optionally, X - B_ins can represent the difference in grayscale values between pixels at the same position in X and B_ins.
[0057] This concludes the process. Figure 2 The process is shown below.
[0058] pass Figure 2 As can be seen from the flowchart, in this embodiment, when fitting the third background data corresponding to the current temperature T each time, the non-uniform noise fpn_M is taken into account. The non-uniform noise fpn_M is included in determining the third background data corresponding to the current temperature T, ensuring that the finally determined third background data is corrected by the non-uniform noise fpn_M. Then, the third background data corrected by the non-uniform noise fpn_M is used to correct the output X of the infrared imaging device at the current temperature T. This is equivalent to performing non-uniform noise correction on the output X of the infrared imaging device at the current temperature T.
[0059] The following describes yet another method provided by an embodiment of this application:
[0060] See Figure 3 , Figure 3 This is another method flowchart provided as an embodiment of the present application. The method is applied to an infrared imaging device. For example... Figure 3 As shown, the process may include the following steps:
[0061] Step 301: Based on the first background data and the second background data, determine the third background data corresponding to the current temperature T by fitting the data. The first background data and the second background data are respectively the local data corresponding to the highest temperature value and the lowest temperature value in the temperature range where the current temperature T is located from the stored background-temperature data. The temperature range 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 within the temperature range of the current temperature T are first determined from the stored background-temperature data. As described above, the background-temperature data, based on the temperature interval between adjacent temperatures (e.g., 5 degrees Celsius), implicitly contains temperature ranges, such as 0-5 degrees Celsius, 5-10 degrees Celsius, and so on. Based on this, this embodiment can easily determine the temperature range of the current temperature T. Then, 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] Then, the third background data corresponding to the current temperature T can be determined by fitting the first and second background data. For example, based on the highest and lowest temperature values mentioned above, the first and second background data can be fitted to obtain the third background data corresponding to the current temperature T. There are many ways to perform this fitting, such as linear fitting and nonlinear fitting. Taking linear fitting as an example, assuming that the first background data corresponding to the 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 (denoted as B_ins) corresponding to the current temperature T can be determined as follows: B_ins = B_H*(T-T_L) / (T_H-T_L) + B_L*(T_H-T) / (T_H-T_L).
[0064] Step 302: Based on the third background data and the learned non-uniform noise fpn_M, the output X of the infrared imaging device at the current temperature T is corrected.
[0065] In this embodiment, 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 may include: substituting the aforementioned X, the third background data, a set gain coefficient K, and a set offset into a third specified algorithm to obtain a preliminary correction result of X (denoted as Y). ‘ For example, Y ‘ =K*(X-B_ins)+Offset; Then, the preliminary correction result is corrected based on the learned non-uniform noise fpn_M to obtain the 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] As can be seen, in this embodiment, the output X of the infrared imaging device at the current temperature T is directly corrected using the learned non-uniform noise fpn_M, thus achieving non-uniform noise correction. 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 described above, and this embodiment is not specifically limited to it.
[0067] This concludes the process. Figure 3 The process is shown below.
[0068] Figures 4a to 4b The diagrams show the effects before and after correction using any of the methods described above. Figures 5a to 5bThe diagrams showing the effects before and after correction using any of the methods described above demonstrate that correction using any of the methods can significantly improve the quality of infrared images acquired by the infrared imaging device, thereby enhancing the accuracy of subsequent image processing based on the infrared images, such as target recognition.
[0069] The proposed solution is particularly suitable for barrier-free thermal imaging devices. Barriers are the structure used in traditional thermal imaging modules to acquire noise correction data. During the imaging process, the module needs to be triggered periodically, which can cause scene freezing. Barrier-free devices eliminate the barrier structure, allowing for a smaller module size and lower power consumption, and eliminating the image freezing problem.
[0070] The methods provided in the embodiments of this application have been described above. The apparatus provided in the embodiments of this application is described below:
[0071] See Figure 6 , Figure 6 This is a structural diagram of an electronic device provided in an embodiment of this 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, this application embodiment also provides a machine-readable storage medium storing a plurality of computer instructions, which, when executed by a processor, can implement the method disclosed in the above examples of this application.
[0073] Based on the same application concept as the above method, this application embodiment also provides a computer program product storing a computer program, which, when executed by a processor, implements the method disclosed in the above examples of this application.
[0074] For example, the aforementioned 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 instance, machine-readable storage media can be: RAM (Random Access Memory), volatile memory, non-volatile memory, flash memory, storage drives (such as hard disk drives), solid-state drives, any type of storage disk (such as optical discs, DVDs, etc.), or similar storage media, or combinations thereof.
[0075] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A method for correcting non-uniform noise, characterized in that, This method is applied to infrared imaging equipment, and the method includes: 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, non-uniform noise fpn_M is learned based on the correction result; wherein, the third background data corresponding to the current temperature T is determined by fitting based on the first background data and the second background data; the first background data and the second background data are respectively the background data corresponding to the highest temperature value and the lowest temperature value in the temperature range where the current temperature T is located from the stored background-temperature data; the temperature range 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 based on the non-uniform noise fpn_M; The step of 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 highest temperature value, and the lowest temperature value into the first algorithm to obtain the updated first background data; 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 highest temperature value, and the lowest temperature value into the second algorithm to obtain the updated second background data.
2. The method according to claim 1, characterized in that, The step of updating the first background data based on the non-uniform noise fpn_M includes: Update the first baseline data according to the following formula: = B_L + fpn_M (T – T_L) / (T_H – T_L); in, The first background data is represented by T_L, the lowest temperature value is represented by T_H, the highest temperature value is represented by B_L, and the first background data is represented by B_L.
3. The method according to claim 1, characterized in that, The step of updating 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 + fpn_M (T_H-T) / (T_H – T_L); in, This represents the updated second background data, where T_L represents the lowest temperature value, T_H represents the highest temperature value, and B_H represents the second background data.
4. A method for correcting non-uniform noise, characterized in that, This method is applied to infrared imaging equipment, and the method includes: The third background data corresponding to the current temperature T is determined by fitting the first background data, the second background data, and the learned non-uniform noise fpn_M. The first background data and the second background data are respectively the background data corresponding to the highest temperature value and the lowest temperature value in the temperature range where the current temperature T is located, which are stored background-temperature data. The temperature range is determined based on the temperature interval between each adjacent temperature in the stored background-temperature data. The learned non-uniform noise fpn_M represents the non-uniform noise learned from the previous correction result. The first background data and the second background data have been updated based on the learned non-uniform noise fpn_M using the method described in any one of claims 1-3. The output X of the infrared imaging device at the current temperature T is corrected based on the third background data.
5. The method according to claim 4, characterized in that, The third background data corresponding to the current temperature T, determined by fitting the first background data, the second background data, and the learned non-uniform noise fpn_M, includes: Based on the highest temperature value and the lowest temperature value, the first background data and the second background data are fitted to obtain the fitting result; The third background data is determined based on the difference between the fitting result and the non-uniform noise fpn_M.
6. A method for correcting non-uniform noise, characterized in that, This method is applied to infrared imaging equipment, and the method includes: The third background data corresponding to the current temperature T is determined by fitting the first background data and the second background data; the first background data and the second background data are respectively the background data corresponding to the highest temperature value and the lowest temperature value in the temperature range where the current temperature T is located from the stored background-temperature data; the temperature range is determined based on the temperature interval between each adjacent temperature in the stored background-temperature data. Based on the third background data and the learned non-uniform noise fpn_M, the output X of the infrared imaging device at the current temperature T is corrected; the learned non-uniform noise fpn_M represents the non-uniform noise learned from the previous correction result; the first background data and the second background data have been updated based on the learned non-uniform noise fpn_M using the method of any one of claims 1-3.
7. The method according to claim 6, characterized in that, The step of 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 X, the third background data, the set gain coefficient K, and the set offset into the third algorithm, we obtain the preliminary correction result of X; The preliminary correction result is corrected based on the learned non-uniform noise fpn_M to obtain the target correction result.
8. 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 configured to execute machine-executable instructions to implement the method steps of any one of claims 1-7.
9. A computer program product, characterized in that, The computer program product contains a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.
Citation Information
Patent Citations
Nonuniformity correction method and system of infrared image
CN107255521A