Infrared detection method based on scene signal hierarchical self-enhancement

Abstract

The application provides an infrared detection method based on scene signal hierarchical self-enhancement, which integrates a regulation strategy model under different thermal radiation background conditions, an optimal bias mapping model under different scenes and a scene classifier model into a readout circuit hierarchical self-enhancement model; the captured scene signal is analyzed to extract a pixel level interest unit signal and a non-interest unit signal, the collected scene signal is input into the readout circuit hierarchical self-enhancement model, and optimal bias parameters, optimal control voltages, optimal integration time and integration capacitance values are calculated; the optimal bias parameters are used to drive a pixel array to obtain an original infrared image, the original infrared image is subjected to signal hierarchical self-enhancement according to the obtained control parameters, and finally, relevant double sampling is performed to obtain high-quality infrared image signal output. The application improves the temperature resolution of the image in the interest region.

Description

TECHNICAL FIELD

[0001] The present application relates to infrared detection technology, in particular to an infrared detection method based on scene signal hierarchical self-enhancement. BACKGROUND

[0002] Infrared detection is a new passive photoelectric imaging technology, which uses the difference of radiation of each part of the target to obtain the details of the image, and converts the infrared radiation signal into visual thermal image. Noise equivalent temperature difference (NETD) is one of the important indicators of infrared detector, which represents the minimum value of the system distinguishable target temperature difference. The smaller the NETD of the system, the more target information can be detected, and the stronger the target recognition and identification ability. Limited by the performance of the noise equivalent temperature difference of the detector, the infrared detection technology faces technical bottlenecks in some fields of application. Therefore, how to effectively reduce the NETD of the detector is of great significance to improve the performance of the infrared detector and promote the wide application of infrared detection technology in more fields.

[0003] Currently, the optimization of the NETD of the detector mainly includes the following two methods: 1) increasing the bias voltage, increasing the integration time and reducing the integration capacitance to increase the detection rate of the system; 2) increasing the static current, increasing the MOS tube lateral length L (reducing 1 / f noise) and reducing the MOS tube transconductance gm (reducing white noise) to reduce the system noise. Due to the mutual restriction of the above two methods, and the mutual balance of the internal parameters of the detector involved in the single NETD control means, the reduction of the noise equivalent temperature difference still has performance bottleneck. SUMMARY

[0004] The purpose of the present application is to provide an infrared detection method based on scene signal hierarchical self-enhancement, so as to dynamically adjust the NETD curve with the change of the interested area in the full temperature range according to different scenes, and then realize the performance breakthrough of the NETD optimization of the infrared detector.

[0005] The technical solution for achieving the purpose of the present application is: an infrared detection method based on scene signal hierarchical self-enhancement, comprising the following steps:

[0006] Step 1, constructing a readout circuit hierarchical self-enhancement model based on an infrared detector

[0007] Based on the NETD analysis, a target heat transfer model is constructed, and then a regulation strategy model under different thermal radiation background conditions is constructed. Based on the infrared image analysis, a scene classifier model is constructed, and then an optimal bias mapping model under different scenes is constructed. The regulation strategy model under different thermal radiation background conditions, the optimal bias mapping model under different scenes, and the scene classifier model are integrated into a readout circuit hierarchical self-enhancement model;

[0008] Step 2, sampling the scene signal by using an infrared detector

[0009] The captured scene signal is analyzed to extract the pixel-level interest unit signal and non-interest unit signal, and the collected scene signal is input into the readout circuit hierarchical self-enhancement model in step 1 to calculate the optimal bias parameter, optimal control voltage, optimal integration time and integration capacitance value.

[0010] Step 3, signal hierarchical self-enhancement technology

[0011] According to the optimal bias parameter in step 2, the original infrared image is obtained by driving the pixel array, the original infrared image is hierarchically self-enhanced according to the control parameter in step 2, the non-interest region is generated based on the non-interest unit signal extracted in step 2, and the original infrared image is non-interest region signal suppressed according to the optimal control voltage in step 2, the interest unit region is generated based on the interest unit signal extracted in step 2, and the original infrared image is interest region signal enhanced according to the optimal integration time and integration capacitance value in step 2, finally, the infrared image signal output of high quality is obtained by related double sampling on the infrared image respectively subjected to non-interest region signal suppression and interest region signal enhancement.

[0012] An infrared detection system based on scene signal hierarchical self-enhancement, based on the infrared detection method, realizes infrared detection based on scene signal hierarchical self-enhancement.

[0013] A computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, when the processor executes the computer program, based on the infrared detection method, realizes infrared detection based on scene signal hierarchical self-enhancement.

[0014] A computer readable storage medium, having a computer program stored thereon, when the computer program is executed by a processor, based on the infrared detection method, realizes infrared detection based on scene signal hierarchical self-enhancement.

[0015] Compared with the prior art, the present application has the following advantages: the infrared detection method based on scene signal hierarchical self-enhancement is adopted, which provides a new balance means for detection rate and system noise, fundamentally breaks through the performance bottleneck of noise equivalent temperature difference, and makes the noise equivalent temperature difference curve dynamically adjustable according to different scenes and interest regions, realizes hierarchical enhancement of thermal images in the interest region, and improves the temperature resolution of images in the interest region. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 It is an infrared detector readout circuit architecture involved in the infrared detection method based on scene signal hierarchical self-enhancement.

[0017] Figure 2 The schematic diagram of the target transfer model principle involved in the scene signal grading self-enhancement based infrared detection method of the present application.

[0018] Figure 3 The regional grading self-enhancement technology implementation block diagram of the scene signal grading self-enhancement based infrared detection method of the present application.

[0019] Figure 4 The regional grading self-enhancement schematic diagram of the scene signal grading self-enhancement based infrared detection method of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0021] As shown in Figure 1 , the scene signal grading self-enhancement based infrared detection method involves an infrared detector readout circuit, which is composed of nine parts, including a scene signal capture and analysis unit, a scene classifier, an on-chip register, a pixel-level ADC, a unit bias circuit, a non-interest region suppression unit, an interest region enhancement unit, a correlated double sampling unit, and an output unit. The grading self-enhancement infrared detection method is implemented based on the built infrared detector readout circuit, and the specific steps are as follows:

[0022] Step 1, build a grading self-enhancement model based on the infrared detector readout circuit, which specifically includes the following 3 sub-steps:

[0023] Step 11, build a target heat transfer model based on NETD analysis, and build a regulation strategy model under different thermal radiation background conditions based on the target heat transfer model. The target heat transfer model is as shown in Figure 2 When the temperature of the detection target A point is T t , the representation of the emissivity of A point is as formula (1),

[0024]

[0025] Wherein, the radiation wavelength λ is between λ1 and λ2, h is the Planck constant, c is the propagation speed of light in vacuum, k is the Boltzmann constant, ε t is the emissivity of the target. The distance between the detection target and the infrared optical lens is L t , then the representation of the infrared radiation power received by the A' point corresponding to the A point on the detection array is as formula (2),

[0026]

[0027] where τ0(λ) and τ α (λ) are the transmittance of the optical path from the target to the lens and between the lenses, respectively, ε d is the absorption of the array, A d is the absorption area of a single pixel of the array, F # is the optical constant, and L λ is the emissivity. Combining the thermal response equation of the pixel, the change of the target temperature causes the steady-state temperature response of the corresponding pixel on the detector array, which is represented as formula (3),

[0028]

[0029] where G is the thermal conductance of the array pixel, T d and T t are the temperature of the corresponding pixel of the array and the temperature of the detected target, respectively. The temperature response rate dT d / dT t is an important parameter of the pixel detection efficiency, so for different thermal radiation background conditions, the infrared detection experiment is carried out, and the offline optimization of the temperature response rate dT d / dT t is realized, so as to form a regulation strategy model under different thermal radiation background conditions, the purpose is to adaptively improve the pixel detection efficiency according to different thermal radiation background conditions;

[0030] Step 12, constructing a scene classifier model based on infrared image analysis, and constructing an optimal bias mapping model under different scenes based on the scene classifier. In the actual application of the infrared focal plane imaging system, the target in the image is mainly concerned, and according to the characteristics of the infrared image, the target gray area is often concentrated in the relatively narrow middle area of the image histogram. The image scene gray area is extracted by histogram interception, the distribution interval of the original signal of the scene is obtained, the scene is classified according to the distribution interval characteristics of the original signal, so as to form a scene classifier. This scene classifier can separate the influence of image singular points and take into account the effective information in the scene, without the need for high precision, so as to reduce the calculation amount of the algorithm, and is suitable for integrated digital circuit. On this basis, for each different scene, infrared detection experiment is carried out, and offline optimization of the optimal bias parameter, control voltage, integration time and integration capacitance is realized, so as to form an optimal bias mapping model under different scenes, the purpose is to dynamically generate optimal bias parameters, control voltage, integration time and integration capacitance according to different scenes;

[0031] Step 13, integrating the regulation strategy model under different thermal radiation background conditions, the optimal bias mapping model under different scenes, and the scene classifier model into a readout circuit hierarchical self-enhancement model. The readout circuit hierarchical self-enhancement model is composed ofFigure 1 The scene classifier in the above formula and the on-chip register, wherein the scene classifier realizes scene classification based on image scene gray region extraction to obtain a focal plane original signal distribution interval, and the on-chip register stores a regulation strategy model under different thermal radiation background conditions and an optimal bias mapping model under different scenes. The readout circuit hierarchical self-enhancement model maps optimal bias parameters, control voltages, integration time and integration capacitance based on the output of the scene classifier, and maps regulation parameters of the detection rate based on the thermal radiation background condition;

[0032] Step 2, the scene signal is sampled by using the infrared detector. On the one hand, the captured scene signal is analyzed to extract the interest unit signal and the non-interest unit signal at the pixel level, as shown in FIG. 2. Figure 3 As shown in FIG. 3, the signal current is integrated by the integrator to obtain the scene gray interval. According to the radiation interval of the target to be detected, the distribution of the scene gray in the focal plane output signal is calculated. The 3σ method is used to separate the position and size of the non-interest target. The preset target signal model is used to calculate the non-interest region suppression parameter value and the interest region enhancement parameter value. The focal plane is driven again by the correction loop to capture the scene, and error discrimination is performed until the region optimal value is obtained. On the other hand, the collected scene signal is input into the readout circuit hierarchical self-enhancement model in step 1 to calculate the optimal bias parameter, the optimal control voltage, the optimal integration time and the integration capacitance value and other control parameters.

[0033] Step 3, the unit bias circuit drives the pixel array to obtain the original infrared image according to the optimal bias parameter in step 2. Then, the original infrared image is subjected to signal hierarchical self-enhancement according to the control parameters obtained in step 2. Finally, relevant double sampling is performed to obtain the output of the high-quality infrared image signal.

[0034] The technical implementation of the signal hierarchical self-enhancement in step 3 is shown in FIG. 4. The specific principle is as follows: Figure 3

[0035] The region distribution diagram of the output signal is shown in FIG. 5(a). The infrared detector output signal is divided into five regions. The regions B and C are the signal output ranges occupied by the non-interest signal, the region E is the detector output noise, and the regions A and D are the output regions of the detector interest signal. In the output thermal tomographic image, the non-interest signal of the image occupies a large signal swing, Figure 4 The expression of the system signal-to-noise ratio (SNR) of (a) is as formula (4): Figure 4

[0036]

[0037] ​​The non-interest region is generated based on the non-interest unit signal extracted in step 2, information extraction is only performed on the interest signal region, and the controllable current source is controlled according to the optimal control voltage in step 2 to realize the suppression of the non-interest region signal in the original infrared image. By reducing the sizes of regions B and C and expanding the sizes of interest regions A and D, the SNR of the system is represented as formula (5):

[0038]

[0039] In formula (5), δ is the suppression efficiency of the interest region, which is related to the threshold voltage value. The noise equivalent temperature difference (NETD) of the infrared detector is related to various aspects of the design of the detector, such as material TCR, spectral absorption rate, filling rate, cantilever thermal conductivity, etc. The representation of NETD is as formula (6):

[0040] NETD 2 = NETD 1 / f 2 + NETD Johnson 2 + NETD thermal 2 + NETD ROIC 2 (6) In formula (6), NETD 1 / f , NETD Johnson , and NETD thermal are the effects of focal plane array performance on NETD, NETD ROIC is the effect of readout circuit on NETD, wherein NETD ROIC accounts for a decisive proportion, and the representation of NETD ROIC is as formula (7):

[0041]

[0042] In formula (7), F no is the aperture number of the optical system, and when the target is infinitely far away and imaged at the focal point: F no = (f / D) 2 + 1 / 4, wherein f is the focal length, D is the diameter of the optical system, V N is the electronic noise in the entire system bandwidth, τ0 is the transmittance of the optical system, A D is the area of the detector pixel, is the voltage detection rate of the pixel, and the voltage detection rate of the infrared detector pixel is represented as formula (8):

[0043]

[0044] In formula (8), C intis a fixed integration capacitor, T int is an integration time, R is an input equivalent resistance, the region of interest is generated based on the signal of the region of interest extracted in step 2, and the detection rate is controlled according to the optimal integration time and the integration capacitor value in step 2, so that the NETD curve is dynamically adjusted in the entire temperature range, and the effect of signal enhancement of the region of interest in the original infrared image is realized. Figure 4 (b) shown, the above infrared images respectively subjected to non-region of interest signal suppression and region of interest signal enhancement are correlated and double-sampled to obtain the output of high-quality infrared image signals.

[0045] The present application also proposes an infrared detection system based on scene signal hierarchical self-enhancement, which is based on the infrared detection method and realizes infrared detection based on scene signal hierarchical self-enhancement.

[0046] A computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, infrared detection based on scene signal hierarchical self-enhancement is realized based on the infrared detection method.

[0047] A computer readable storage medium has a computer program stored thereon, and when the computer program is executed by a processor, infrared detection based on scene signal hierarchical self-enhancement is realized based on the infrared detection method.

[0048] The technical features of the above embodiments can be combined in any manner, and to make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not contradict, they should be considered as the scope of the present application.

[0049] The above embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the present application. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are all within the scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An infrared detection method based on scene signal hierarchical self-enhancement, characterized in that, Comprising the following steps: Step 1, constructing a readout circuit hierarchical self-enhancement model based on an infrared detector Based on NETD analysis, a target heat transfer model is constructed, and then a regulation strategy model under different thermal radiation background conditions is constructed, a scene classifier model is constructed based on infrared image analysis, and then an optimal bias mapping model under different scenes is constructed, the regulation strategy model under different thermal radiation background conditions, the optimal bias mapping model under different scenes, and the scene classifier model are integrated into a readout circuit hierarchical self-enhancement model; Step 2, sampling the scene signal using the infrared detector The captured scene signal is analyzed to extract the interest unit signal and the non-interest unit signal at the pixel level, the collected scene signal is input into the readout circuit hierarchical self-enhancement model of step 1, and the optimal bias parameter, the optimal control voltage, the optimal integration time and the integration capacitance value are calculated; Step 3, signal hierarchical self-enhancement technology According to the optimal bias parameter in step 2, the original infrared image is obtained by driving the pixel array, the original infrared image is hierarchically self-enhanced according to the control parameter in step 2, the non-interest region is generated based on the non-interest unit signal extracted in step 2, and the original infrared image is subjected to non-interest region signal suppression according to the optimal control voltage in step 2, the interest unit region is generated based on the interest unit signal extracted in step 2, and the detection rate is controlled according to the optimal integration time and integration capacitance value in step 2, so that the noise equivalent temperature difference curve of the infrared detector is dynamically adjusted in the entire temperature range, the original infrared image is subjected to interest region signal enhancement, and finally, the infrared images subjected to non-interest region signal suppression and interest region signal enhancement are subjected to relevant double sampling, and the output of high-quality infrared image signal is obtained.

2. The scene signal-based hierarchical self-enhancement infrared detection method according to claim 1, characterized in that, Step 1, constructing a readout circuit hierarchical self-enhancement model based on an infrared detector, the specific method is: Step 11, based on NETD analysis, a target heat transfer model is constructed, and then a regulation strategy model under different thermal radiation background conditions is constructed; When the temperature of the detection target point A is T t , the expression of the radiation rate L λ of the point A is as formula (1). where the radiation wavelength λ is between λ1and λ2, h is Planck's constant, c is the speed of light in a vacuum, k is the Boltzmann constant, ε t emissivity targeted The power Φ of the infrared radiation received at the point A' on the array corresponding to the target point A d is represented as in equation (2) where τ0(λ) and τ α (λ) are the transmittances of the optical path from the target to the lens and between the lenses, respectively, ε d is the absorption of the array, A d is the absorption area of a single pixel of the array, F # is the optical constant, and L λ is the radiance; Combined with the thermal response equation of the pixel, the change of the target temperature causes the steady-state temperature response of the corresponding pixel on the detector array to be represented as formula (3), Wherein, G is the array pixel thermal conductance, T d and T t are the temperature of the array corresponding pixel and the temperature of the detected target respectively, the temperature response rate dT d / dT t is an important parameter of the pixel detection efficiency; Step 12, constructing a scene classifier model based on infrared image analysis, and constructing an optimal bias mapping model under different scenes based on the scene classifier; The image scene gray region is extracted by histogram interception, the distribution interval of the focal plane original signal of the scene is obtained, the scene is classified according to the distribution interval characteristics of the original signal, and the scene classifier is formed, on this basis, infrared detection experiments are carried out for each different scene, and offline optimization of the optimal bias parameter, control voltage, integration time and integration capacitance is realized, thereby forming an optimal bias mapping model under different scenes, the purpose is to dynamically generate the optimal bias parameter, control voltage, integration time and integration capacitance according to different scenes; Step 13, integrating the regulation strategy model under different thermal radiation background conditions, the optimal bias mapping model under different scenes, and the scene classifier model into a readout circuit hierarchical self-enhancement model; The readout circuit hierarchical self-enhancement model is composed of a scene classifier and on-chip registers, wherein the scene classifier realizes scene classification based on image scene gray region extraction to obtain a focal plane original signal distribution interval; the on-chip registers store a regulation strategy model under different thermal radiation background conditions and an optimal bias mapping model under different scenes; the readout circuit hierarchical self-enhancement model maps optimal bias parameters, control voltages, integration time and integration capacitance based on the output of the scene classifier, and maps regulation parameters of a detection rate based on a thermal radiation background condition.

3. The scene signal-based hierarchical self-enhancement method for infrared detection according to claim 1, characterized in that, In step 2, the scene signal is sampled by using an infrared detector, and the specific method is as follows: The scene signal is sampled by using an infrared detector, the captured scene signal is analyzed to extract a pixel-level interest unit signal and a non-interest unit signal, the signal current is integrated by using an integrator to obtain a scene gray interval, the distribution of the scene gray in a focal plane output signal is calculated according to a radiation interval of a required detection target, the position and size of a non-interest target are separated by using a 3σ method, non-interest region suppression parameter values and interest region enhancement parameter values are calculated by using a preset target signal model, the focal plane is driven again by using a correction loop to capture the scene again, and error discrimination is performed until the region optimal values are obtained; The collected scene signal is input into the readout circuit hierarchical self-enhancement model in step 1 to calculate optimal bias parameters, optimal control voltages, optimal integration time and integration capacitance values. 4.A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the infrared detection method according to any one of claims 1-3 is implemented to realize the infrared detection based on the scene signal hierarchical self-enhancement. 5.A computer readable storage medium, having a computer program stored thereon, wherein when the computer program is executed by a processor, the infrared detection method according to any one of claims 1-3 is implemented to realize the infrared detection based on the scene signal hierarchical self-enhancement.

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