A non-uniformity correction method without a mask for a non-cooled infrared focal plane detector

Abstract

The present invention relates to a non-uniformity non-block correction method for a non-cooled infrared focal plane detector, which comprises the following steps: Step A, collecting a plurality of process data in a temperature test chamber, where the process refers to the data collection process of the infrared non-cooled detector from cold start to burn-in at a certain temperature in the temperature test chamber, and the plurality of processes means that there are N set temperatures in the temperature test chamber, and the data collection process at each temperature is the same; Step B, optimal temperature rise estimation; Step C: dynamic background estimation and non-uniformity correction: according to the optimal offset obtained by the optimal temperature rise estimation, finding the nearest process in the plurality of process data, and the background obtained in each nearest process constitutes a dynamic background; Step D: following: calculating indicators for the obtained correction result to determine whether to adjust the search step size and the drift amount change array.

Description

Technical Field

[0001] The present invention relates to the field of infrared technology, and particularly to a non-uniformity non-block correction method for a non-cooled infrared focal plane detector. Background Art

[0002] Existing non-uniformity non-block correction methods for non-cooled infrared focal plane detectors include a calibration method and a scene method:

[0003] (1) In the calibration method, the detector is placed in a temperature test chamber, and background data is collected at several temperature values of the test chamber, and the corresponding detector temperature values are recorded. During actual use, according to the detector temperature, a table is looked up, and the background data at the current moment is estimated from the background of the test chamber by using the interpolation idea, so as to perform non-uniformity correction. The characteristics of the calibration method are that the background can be stored, the calculation amount during correction is small, and it is convenient for hardware implementation, but there are problems that cannot be ignored:

[0004] A) The test chamber environment is different from the actual use environment. The air flow in the test chamber is strong and the heat dissipation is fast, while the actual environment is uncertain (no wind, weak wind, etc.);

[0005] B) The calibration method depends on the detector temperature to look up the table or perform fitting, and the detector temperature and the detector state cannot be in one-to-one correspondence, but a one-to-many relationship, that is, the same detector temperature corresponds to multiple detector states. For example, the detector works for 20 minutes in a 20°C test chamber and the detector works for 1 hour in a 10°C actual environment. Although the same detector temperature can be reached, the detector states are completely different. The background in the test chamber represents the detector state during background collection in the test chamber, which is not equivalent to the detector state during actual operation;

[0006] The scene method continuously estimates and corrects the background data by using the statistical information of the scene, such as time-domain high-pass, constant statistics, and artificial neural network. The estimation and correction capabilities of the scene method affect the correction effect. At the same time, the scene method has a large calculation amount and depends on the scene movement, making it difficult to implement in hardware.

[0007] Affected by factors such as ambient temperature, heat dissipation, and burn-in, the background collected in the test chamber cannot reflect the detector state during actual use, resulting in noise and stripes often remaining in the correction result.

[0008] Considering the accumulation characteristics of infrared heat, from the start of each power-on to a period of operation, the heat accumulation of the detector can be regarded as an indefinite integral model - starting from one state and integrating to another state under a certain integral function. Therefore, the background collected in the test chamber can be regarded as the result of a specific integration to a certain state, while during actual use, it is another integration process, and the integration process during actual use will vary greatly.

[0009] From the perspective of "process", the background data collected by the incubator only represents a moment under the current environment and detector state, that is, a sample at a certain moment in this process; and for the "process" during the actual use of the detector, each moment is also a sample. The traditional background correction idea is to use the "sample" of the process in the incubator to describe the sample of the process during actual use.

[0010] Considering the complexity of integral function modeling and combining the temperature rise characteristics found in the experiment, from the perspective of describing "process" by "process", this invention replaces the traditional idea of describing "sample" by "sample" and proposes a non-uniformity correction method without shielding for uncooled infrared focal plane detectors. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of the existing technology, such as the influence of the working environment on non-uniformity, large technical calculation amount, dependence on scene movement, and difficulty in hardware implementation, and to provide a non-uniformity correction method without shielding for uncooled infrared focal plane detectors.

[0012] The purpose of this invention is achieved through the following technical solutions:

[0013] A non-uniformity correction method without shielding for uncooled infrared focal plane detectors, characterized by including the following steps:

[0014] Step A: Collect multiple process data in a temperature test chamber. The "process" refers to the data collection process of the infrared uncooled detector from cold start to burn-in at a certain temperature in the temperature test chamber. The "multiple processes" mean that there are N set temperatures in the temperature test chamber, and the data collection process at each temperature is the same;

[0015] Step B: Optimal temperature rise estimation;

[0016] Step C: Dynamic background estimation and non-uniformity correction: According to the optimal offset obtained from the optimal temperature rise estimation, find the nearby process in the multiple process data, and the backgrounds obtained in each nearby process constitute the dynamic background;

[0017] Step D: Tracking: Calculate the index for the obtained correction result and determine whether to adjust the search step size and the drift amount change array.

[0018] Specifically, Step A includes the following sub-steps:

[0019] Step A1: Shutdown and static stage: The temperature test chamber is stably operating at the set temperature Tbox, and the infrared detector module is placed in a shutdown state for t1 minutes;

[0020] Step A2, from power-on to stable operation stage: within t2 minutes after the infrared detector module is powered on, at a certain time interval dt2, uniformly planar object image data and corresponding detector temperature data are collected at regular intervals;

[0021] Step A3, from stable operation to burn-in stage: within t3 minutes after Step A2, at a certain time interval dt3, uniformly planar object image data and corresponding detector temperature data are collected at regular intervals;

[0022] Step A4, deep burn-in stage: within t4 minutes after Step A3, the temperature test chamber is powered off, and at a certain time interval dt4, uniformly planar object image data and corresponding detector temperature data are collected at regular intervals;

[0023] Step A5, temperature recovery stage: within t5 minutes after Step A4, the module is powered off, and the temperature test chamber is set to a lower temperature TboxLow to cool the module;

[0024] The uniformly planar object includes a blackbody, paper, or foam;

[0025] The temperature test chamber includes a high and low temperature chamber or a walk-in temperature stable environment.

[0026] Specifically, the uniformly planar object image data is denoted as {Y i k}, where k is the process serial number, k = 1,..., N, N is the number of temperatures set in the temperature test chamber, i is the background serial number in the k-th process, i = 1,..., M, M is the number of data in this process, and the size of each frame of the background image is H×W; the detector temperature data is denoted as

[0027] Specifically, Step B includes the following sub-steps:

[0028] Step B1, with the current detector temperature v superimposed with a drift amount dv, find the nearest process among the multiple processes obtained in Step A, and denote the background data of the nearest process as The corresponding detector temperature data is denoted as

[0029] Step B2, in the nearest process obtained in Step B1, search for the optimal power-on point drift amount, and dv is initialized to 0. Specifically, Step B1 further includes:

[0030] For the k-th process {Y i k} obtained in Step A, determine whether the current detector temperature v+dv under the superposition of the drift amount is within the detector temperature range [min(V i k), max(V i k )], if so, this process is considered a nearby process and is added to the sets {Yn}, {Vn}; traverse k from [1, M].

[0031] Specifically, step B2 further includes the following sub-steps:

[0032] Step B21: Take 3 frames of original image data continuously collected by the detector in time, denoted as {ysrc1, ysrc2, ysrc3}, and the corresponding 3 detector temperatures as {vc1, vc2, vc3}. The drift amount change array Index is initialized to {-step, 0, +step}, and the initial value of step is step0.

[0033] Step B22: For the 3 consecutive frames of original data, according to the drift amount dv and their respective drift amount changes, find the nearby background in the nearby process obtained in step B1 and perform correction.

[0034] Step B23: Calculate the metrics {pp1, pp2, pp3} for the correction results of the 3 consecutive frames of original data. The metric is the correction result pp i The sum of local variances in the flat area. i is the sequence number of the 3 consecutive frames, i = 1, 2, 3. Taking the calculation of the metric of one frame of image as an example:

[0035]

[0036] Among them, m0, n0, m1, n1 are the coordinate ranges of the flat area of the correction result image, v st is the local variance at the coordinate (s, t);

[0037] Step B23: The flat area can be obtained through a sharpness evaluation function - that is, statistically calculate the sharpness metric within the window and let the window slide within the entire image range. Take the position with the minimum sharpness metric as the flat area.

[0038] Step B24: According to the metrics obtained in step B23, adjust the drift amount dv, the search step size step, and the drift amount change array Index, and judge whether the search stop condition is reached. If so, go to step C; otherwise, continue to take 3 consecutive frames of original data of the detector and repeat steps B21 - B24.

[0039] Specifically, taking the correction of one frame of image as an example, step B22 further includes the following sub-steps:

[0040] Step B221: The detector temperature under the action of the drift amount is vv = vc i + dv + Index(i), where i is the sequence number of the 3 consecutive frames, i = 1, 2, 3;

[0041] Step B222: Search for the nearest background in each nearest process, and calculate the corresponding background under this process. Taking the kk-th process in the nearest process as an example, it specifically includes:

[0042] The background data of the kk-th process is The detector temperature is

[0043] Within Find the node closest to vv, and obtain the background data by Lagrange interpolation;

[0044] Step B223: Perform Lagrange interpolation again on the m backgrounds obtained in Step B222 (m is the number of nearest processes) to obtain the final corrected background;

[0045] Step B224: Use the background obtained in Step B223 to correct the original data according to the one-point or two-point correction method.

[0046] Specifically, Step B24 further includes the following sub-steps:

[0047] Step B241: Find the minimum value of {pp1, pp2, pp3}, denoted as pp min ;

[0048] Step B242: Adjust the drift amount dv, the search step size step, and the drift amount change array Index:

[0049] If pp min == pp1, then the Index array is uniformly decreased by step, and the flag bit sp is set to 1;

[0050] If pp min == pp3, then the Index array is uniformly increased by step, and the flag bit sp is set to -1;

[0051] If pp min == pp2, then the Index array remains unchanged, step is reduced; dv is accumulated with Index(2);

[0052] Step B243: Judgement,

[0053] Step B243 further includes:

[0054] If the value of the flag bit sp is different from the historical flag bit sp, then there is oscillation, and step is reduced;

[0055] If step is less than the threshold, then the search stop condition is true; otherwise the search stop condition is false.

[0056] Specifically, Step C further includes the following sub-steps:

[0057] Step C1: When the current detector temperature is v, obtain the nearby process according to the calculation method in Step B1.

[0058] Step C2: Under the superposition of the drift amount, the detector temperature is v+dv, where dv is the optimal drift amount obtained in Step B. Calculate the corrected background according to the calculation methods in Steps B221 - B223.

[0059] Step C3: Use the corrected background obtained in Step C2 to perform non-uniformity correction as described in Step B224.

[0060] Specifically, Step D further includes the following sub-steps:

[0061] Step D1: Calculate the index pp for the corrected result obtained in Step C according to the calculation method described in Step B23.

[0062] Step D2: If the index pp is greater than the threshold, start the feedback update: Initialize Index as {-step, 0, +step}, and the initial value of step is step0.

[0063] Step D3: If the frame count accumulation sumT is greater than the threshold, start the timing update.

[0064] Step D3 further includes:

[0065] Initialize Index as {-step, 0, +step}, the initial value of step is step1, and sumT = 0.

[0066] The frame count accumulation refers to the number of consecutive frames sumT that the detector works.

[0067] The present invention has the following advantages: By using the background data of multiple "processes" in the incubator and searching for optimization frame by frame and following, the "process" during the actual operation of the detector is described, reducing the influence of factors such as the background of the incubator not adapting to the actual working state of the detector, ambient temperature change, and burn-in on non-uniformity in conventional background correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is the overall flowchart of the present invention;

[0069] Figure 2 is the flowchart of the process data acquisition of the temperature test chamber of the present invention;

[0070] Figure 3 is the flowchart of the optimal temperature rise estimation of the present invention;

[0071] Figure 4 is the flowchart of the optimal drift amount search of the present invention;

[0072] Figure 5 Flow chart of dynamic local correction for each frame of image of the present invention;

[0073] Figure 6 Flow chart of adjusting search value of the present invention;

[0074] Figure 7 Flow chart of dynamic background estimation and non-uniformity correction of the present invention;

[0075] Figure 8 Flow chart of following of the present invention. Specific implementation mode

[0076] The following further describes the present invention in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0077] Please refer to Figure 1-8 , a non-uniformity correction method without a mask for a non-cooled infrared focal plane detector, characterized by comprising the following steps:

[0078] Step A, collecting a plurality of process data in a temperature test chamber, where the process refers to the data collection process of the infrared non-cooled detector from cold start-up to burn-in at a certain temperature in the temperature test chamber, and the plurality of processes means that there are N set temperatures in the temperature test chamber, and the data collection processes at each temperature are the same;

[0079] Step B, optimal temperature rise estimation;

[0080] Step C: Dynamic background estimation and non-uniformity correction: According to the optimal offset obtained from the optimal temperature rise estimation, find the nearest process among the plurality of process data, and the backgrounds obtained in each nearest process form a dynamic background;

[0081] Step D: Following: Calculate indicators for the obtained correction result, and judge whether to adjust the search step size and the drift amount change array.

[0082] Specifically, the step A includes the following sub-steps:

[0083] Step A1, shutdown and static stage: The temperature test chamber is stably operating at the set temperature Tbox, and the infrared detector module is placed in a shutdown state for t1 minutes;

[0084] Step A2, from start-up to stable operation stage: Within t2 minutes after the infrared detector module is turned on, uniformly plane object image data and corresponding detector temperature data are collected at regular time intervals dt2;

[0085] Step A3, stable operation until the burn-in stage: Within t3 minutes after Step A2, at regular time intervals dt3, collect the image data of the uniform planar object and the corresponding detector temperature data at regular intervals;

[0086] Step A4, deep burn-in stage: Within t4 minutes after Step A3, power off the temperature test chamber, and at regular time intervals dt4, collect the image data of the uniform planar object and the corresponding detector temperature data at regular intervals;

[0087] Step A5, temperature recovery stage: Within t5 minutes after Step A4, power off the module, and set the temperature test chamber to a lower temperature TboxLow to cool the module;

[0088] The uniform planar object includes a black body, paper, or foam;

[0089] The temperature test chamber includes a high and low temperature chamber or a walk-in temperature stable environment.

[0090] Specifically, the image data of the uniform planar object is denoted as {Y i k}, where k is the process serial number, k = 1,..., N, N is the number of temperatures set by the temperature test chamber, i is the background serial number in the k-th process, i = 1,..., M, M is the number of data in this process, and the size of each frame of the background image is H × W; the detector temperature data is denoted as

[0091] Specifically, Step B includes the following sub-steps:

[0092] Step B1, with the current detector temperature v superimposed with the drift amount dv, find the nearest process among the multiple processes obtained in Step A, and denote the background data of the nearest process as The corresponding detector temperature data is denoted as

[0093] Step B2, in the nearest process obtained in Step B1, search for the optimal startup point drift amount, and initialize dv to 0.

[0094] Specifically, Step B1 further includes:

[0095] For the k-th process {Y i k} obtained in Step A, judge whether the current detector temperature v + dv under the superposition of the drift amount is within the detector temperature range [min(V i k ), max(V i k) If so within, the process is considered a nearby process and added to the sets {Yn}, {Vn}; traverse k in [1, M].

[0096] Specifically, step B2 further includes the following sub-steps:

[0097] Step B21, take 3 frames of original image data continuously acquired by the detector in time, denoted as {ysrc1, ysrc2, ysrc3}, and the corresponding 3 detector temperatures as {vc1, vc2, vc3}, the drift amount change array Index, and Index is initialized to {-step, 0, +step}, and the initial value of step is step0;

[0098] Step B22, find the nearby background in the nearby process obtained in step B1 according to the drift amount dv and their respective drift amount changes of the continuous 3 frames of original data, and perform correction;

[0099] Step B23, calculate the metrics {pp1, pp2, pp3} for the correction results of the continuous 3 frames of original data, and the metric is the correction result pp i The sum of local variances in the flat area, i is the serial number of the continuous 3 frames, i = 1, 2, 3. Taking the calculation of the metric of one frame of image as an example:

[0100]

[0101] Among them, m0, n0, m1, n1 are the coordinate ranges of the flat area of the corrected result image, and v st is the local variance at the coordinate (s, t);

[0102] Step B23, the flat area can be obtained through the sharpness evaluation function - that is, count the sharpness metrics within the statistical window and let the window slide within the entire image range, and take the position with the smallest sharpness metric as the flat area;

[0103] Step B24, adjust the drift amount dv, the search step size step, and the drift amount change array Index according to the metrics obtained in step B23, and judge whether the search stop condition is reached. If so, go to step C; otherwise, continue to take 3 consecutive frames of original data of the detector and repeat steps B21 - B24.

[0104] Specifically, taking the correction of one frame of image as an example, step B22 further includes the following sub-steps:

[0105] Step B221, the detector temperature under the action of the drift amount is vv = vc i +dv+Index(i), i is the serial number of the continuous 3 frames, i = 1, 2, 3;

[0106] Step B222: Search for the nearest background in each nearby process, and calculate the corresponding background for this process. Taking the kk-th process in the nearby process as an example, it specifically includes:

[0107] The background data of the kk-th process is The detector temperature is Within Find the node closest to vv, and obtain the background data by Lagrange interpolation;

[0108] Step B223: Perform Lagrange interpolation again on the m backgrounds obtained in Step B222 (m is the number of nearby processes) to obtain the final corrected background;

[0109] Step B224: Use the background obtained in Step B223 to correct the original data in one-point or two-point correction mode.

[0110] Specifically, Step B24 further includes the following sub-steps:

[0111] Step B241: Find the minimum value of {pp1, pp2, pp3}, denoted as pp min ;

[0112] Step B242: Adjust the drift amount dv, search step size step, and drift amount change array Index:

[0113] If pp min == pp1, the Index array is uniformly decreased by step, and the flag bit sp is set to 1;

[0114] If pp min == pp3, the Index array is uniformly increased by step, and the flag bit sp is set to -1;

[0115] If pp min == pp2, the Index array remains unchanged, step is reduced; dv is accumulated with Index(2);

[0116] Step B243: Judge

[0117] Step B243 further includes:

[0118] If the value of the flag bit sp is different from the historical flag bit sp, there is oscillation, and step is reduced;

[0119] If step is less than the threshold, the search stop condition is true; otherwise the search stop condition is false.

[0120] Specifically, Step C further includes the following sub-steps:

[0121] Step C1: The current detector temperature is v. According to the calculation method in Step B1, obtain the nearest process.

[0122] Step C2: Under the superposition of the drift amount, the detector temperature is v + dv, where dv is the optimal drift amount obtained in Step B. According to the calculation methods in Steps B221 - B223, calculate and obtain the corrected background.

[0123] Step C3: Use the corrected background obtained in Step C2 and perform non-uniformity correction as described in Step B224. Specifically, Step D further includes the following sub-steps:

[0124] Step D1: According to the calculation method described in Step B23, calculate the result obtained in Step C to obtain the index pp.

[0125] Step D2: If the index pp is greater than the threshold, start feedback update: Index is initialized to {-step, 0, +step}, and the initial value of step is step0.

[0126] Step D3: If the frame count accumulation sumT is greater than the threshold, start timing update.

[0127] Step D3 further includes:

[0128] Index is initialized to {-step, 0, +step}, the initial value of step is step1, and sumT = 0.

[0129] The frame count accumulation refers to the number of consecutive frames sumT that the detector works.

[0130] The above shows and describes the basic principle, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope claimed by the present invention is defined by the appended claims and their equivalents.

[0131] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and units involved are not necessarily essential to this application.

[0132] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0133] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above various methods. Among them, the storage medium can be a magnetic disk, an optical disk, a ROM, a RAM, etc.

[0134] The foregoing disclosure is only for the preferred embodiments of the present invention, and of course cannot be used to limit the scope of the rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A non-uniformity correction method without a mask for a non-cooled infrared focal plane detector, comprising: Step A: Collect multiple process data in a temperature test chamber. The process refers to the data collection process of the detector from cold start-up to burn-in at a certain temperature in the temperature test chamber; Step B: Optimal temperature rise estimation: Step B1: With the current detector temperature v superimposed with the drift dv, search for the nearest process among the multiple processes obtained in Step A, and record the background data of the nearest process as , and record the corresponding detector temperature data as , , : For the k-th process obtained in step A , determine whether the current detector temperature v + dv under the superposition of the drift amounts is within the detector temperature range of this process . If so, this process is considered a nearby process and is added to the set . ; Traverse k from [1, M]; Step B2: In the nearby process obtained in Step B1, search for the optimal start-up point drift amount, initialize dv to 0, including: Step B21: Take three frames of original image data continuously acquired by the detector in time, denoted as , and the corresponding three detector temperatures are , and the drift amount change array Index; Step B22: For three consecutive frames of original data, according to the drift amount dv and their respective drift amount change amounts, find the nearby background in the nearby process obtained in Step B1, and perform correction; Step B23, calculate the index of the correction results of three consecutive frames of original data , and the index is the correction result which is the sum of local variances in the flat area. Here, i is the serial number of three consecutive frames, i = 1, 2, 3; the flat area can be obtained by the clarity index within the statistical window, and the window slides within the entire image. The position with the minimum clarity index is the flat area; Step B24: According to the index obtained in Step B23, adjust the drift amount dv, the search step size step, and the drift amount change amount array Index, and judge whether the search stop condition is reached. If so, go to Step C; otherwise, continue to take three consecutive frames of original data of the detector and repeat Steps B21 - B24; Step C: Dynamic background estimation and non-uniformity correction: According to the optimal offset amount obtained from the optimal temperature rise estimation, find the nearby process in multiple process data, and the background obtained in each nearby process constitutes the dynamic background; Step D: Follow-up: Calculate the index for the obtained correction result and judge whether to adjust the search step size and the drift amount change amount array.

2. The non-uniformity non-block correction method for a non-cooled infrared focal plane detector according to claim 1, characterized in that The said Step A includes the following sub-steps: Step A1: Shutdown and static stage: The temperature test chamber is stably operating at the set temperature Tbox, and the detector module is placed in a shutdown state for t1 minutes; Step A2: Start-up to stable operation stage: Within t2 minutes after the detector module is started up, collect the image data of a uniform plane object and the corresponding detector temperature data at regular time intervals dt2; Step A3: Stable operation to burn-in stage: Within t3 minutes after Step A2, collect the image data of a uniform plane object and the corresponding detector temperature data at regular time intervals dt3; Step A4: Deep burn-in stage: Within t4 minutes after Step A3, the temperature test chamber is powered off, and collect the image data of a uniform plane object and the corresponding detector temperature data at regular time intervals dt4; Step A5: Temperature recovery stage: Within t5 minutes after Step A4, the module is powered off, and the temperature test chamber is set to a lower temperature TboxLow for cooling the module; The said uniform plane object includes a black body, paper or foam; The said temperature test chamber includes a high and low temperature chamber.

3. A non-uniformity non-block correction method for a non-cooled infrared focal plane detector according to claim 2, characterized in that The image data of the uniform planar object is denoted as , where k is the process serial number, , N is the number of temperatures set in the temperature test chamber, i is the background serial number in the k-th process, , M is the number of data in this process, and the size of each frame of background image is ; the detector temperature data is denoted as .

4. A non-uniformity non-block correction method for a non-cooled infrared focal plane detector according to claim 1, characterized in that, The said Step D further includes the following sub-steps: Step D1: According to the calculation method of Step B23, calculate the obtained correction result in Step C to obtain the index pp; Step D2, if the index pp is greater than the threshold value, start feedback update: Initialize Index as , and set the initial value of step to step0; Step D3: If the frame number accumulation sumT is greater than the threshold, start timing update; The said Step D3 further includes: Index is initialized to , the initial value of step is step1, and sumT = 0; The frame number accumulation refers to the sumT of the consecutive working frames of the detector.

5. A non-uniformity non-block correction method for a non-cooled infrared focal plane detector according to claim 1, characterized in that, The said Step B24 further includes the following sub-steps: Step B241, find the minimum value of, denoted as ; Step B242: Adjust the drift amount dv, the search step size step, and the drift amount change amount array Index: If , the Index array is uniformly decreased by step, and the flag bit sp is set to 1; If , the Index array is uniformly incremented by step, and the flag bit sp is set to -1; If , the Index array remains unchanged, step is reduced; dv accumulates Index(2); Step B243: Judge, The said Step B243 further includes: If the value of the flag bit sp is different from the value of the historical flag bit sp, then there is oscillation and step is reduced; If step is less than the threshold, the search stop condition is true; otherwise, the search stop condition is false.

6. The non-uniformity non-block correction method for a non-cooled infrared focal plane detector according to claim 1, characterized in that, The specific steps of step B22 include the following sub-steps: Step B221, the detector temperature is , where i is the serial number of three consecutive frames, and i = 1, 2, 3; Step B222, find the nearby background in each nearby process, calculate the corresponding background under this process. Taking the kk-th process in the nearby process as an example, it specifically includes: The background data of the kk-th process is , the detector temperature is , ; Find the nearest node of vv within and obtain the background data by Lagrange interpolation method; Step B223, perform Lagrange interpolation again on the m backgrounds obtained in step B222, where m is the number of nearby processes, to obtain the final corrected background; Step B224, use the background obtained in step B223 to correct the original data in one-point or two-point correction mode.

Images