Image intensifier flicker noise quantitative evaluation method and test system

Abstract

The present invention provides a system and method for quantitatively evaluating flicker noise in an image intensifier, comprising a test darkroom, a light source, a light shield, an image intensifier to be tested, a camera, a light source controller, an image intensifier controller, a master controller, and a computer system. The image intensifier to be tested is vertically mounted between the light source and the camera along its optical center axis. A light beam emitted by the light source is projected onto the cathode surface of the image intensifier to be tested through the gap between the two light shields. The camera is configured to image and output flicker noise events in the image intensifier to be tested. The computer system is connected to the camera, receives the image output by the camera, and analyzes and evaluates flicker noise points in the image, quantitatively evaluating the flicker noise frequency and the equivalent number of input electrons. The present invention can accurately quantitatively evaluate flicker noise in a low-light-level image intensifier, establishing an accurate test evaluation and noise quantification data foundation for flicker noise suppression in low-light-level image intensifiers.

Description

Technical Field

[0001] The present invention relates to the field of vacuum photoelectric detection technology, in particular to image intensifier technology, and in particular to a flicker noise quantitative evaluation method and a test system for an image intensifier. Background Art

[0002] Flicker noise in low-light-level image intensifiers (MIIs) is a faint, randomly flickering speckle distributed across the active area of ​​the image intensifier, affecting image quality. The effect is more pronounced in low-light conditions. Flicker noise is a normal characteristic of microchannel plate (MCP) image intensifiers. Studies have shown that flicker noise in image intensifiers under low-light conditions can degrade visual processing capabilities, reducing perception of information such as sharpness, motion, texture, and depth, contributing to increased accident rates. Flicker noise can also be a visual trigger for migraines in wearers.

[0003] According to different causes, flicker noise includes ion flicker and electron flicker. The flicker noise observed under low illumination is mainly the flicker point with high brightness. This flicker noise is the main factor affecting the visual effect at low illumination. - 5 lx gradually increases to 10 -3 As the image intensity increases, flicker noise gradually increases. Regarding flicker noise suppression, research on the digitization of image-enhanced images has examined the pixel characteristics of large flicker points and used algorithms to process the images, indirectly eliminating flicker noise and enhancing the image. Research on directly suppressing flicker noise in image intensifiers has primarily focused on suppressing ion feedback noise, with some also examining electronic fluctuation noise and its impact. Domestic low-light-level image intensifiers exhibit more severe flicker noise at low illumination levels than comparable foreign products. Research on suppressing high-brightness flicker noise is urgently needed, but accurate and reliable flicker noise evaluation methods are currently lacking.

[0004] In the research and testing of flicker noise, Jesse G. Wales et al. in the paper "Statistical Assessment of Night Vision Goggle Noise" used histograms for characterization, but failed to capture the high-brightness flicker points that severely impact visual perception. In the paper "Real-time Adaptive Noise Processing in Low Light Level Images," Chen Qian et al. provided some characteristics of ion noise, including sizes as large as dozens of pixels and lifetimes as long as 30ms-100ms, but did not investigate specific quantitative evaluation methods for flicker noise. In the paper "Low Noise Proximity Focused Image Intensifiers," H. POLLEHN et al. studied flicker noise energy distribution with and without anti-ion feedback membranes, but did not include characteristics of flicker noise severity.

[0005] The research contents disclosed in the above-mentioned papers mainly focus on some characteristic information of the image intensifier's flicker noise, which cannot achieve accurate and effective quantitative evaluation of the image intensifier's flicker noise. Summary of the Invention

[0006] The purpose of the present invention is to provide a quantitative evaluation method and evaluation system for the flicker noise of a low-light-level image intensifier. By using a camera to capture images and perform quantitative evaluation processing, a quantitative evaluation result reflecting the degree of flicker noise is obtained, thereby achieving quantitative evaluation of the flicker noise of the low-light-level image intensifier and laying the foundation for flicker noise suppression in the low-light-level image intensifier.

[0007] According to a first aspect of the present invention, a system for quantitatively evaluating flicker noise in an image intensifier is provided, comprising a test darkroom, a light source, a light shield, an image intensifier to be tested, a camera, a light source controller, an image intensifier controller, a master controller, and a computer system; the light source, light shield, image intensifier to be tested, and camera are all mounted inside the test darkroom; the master controller is connected to the image intensifier controller and the light source controller;

[0008] The test dark box provides a darkroom environment for testing;

[0009] The light source is connected to a light source controller for controlling the luminous intensity of the light source;

[0010] The image intensifier to be tested is vertically installed between the light source and the camera along its optical center axis, with its cathode facing the direction of the light source;

[0011] The light shielding plates are arranged in pairs between the light source and the image intensifier to be tested, and the two light shielding plates are spaced a certain distance apart so that the light beam emitted by the light source passes through the space between the two light shielding plates and is projected onto the cathode surface of the image intensifier to be tested;

[0012] The camera is located at the bottom of the test dark box, with its lens facing the fluorescent surface of the image intensifier to be tested, and is used to image and output the flicker noise events of the image intensifier to be tested;

[0013] The computer system is connected to the camera, receives the image output by the camera, and analyzes and evaluates the flicker noise points in the image, quantitatively evaluating the flicker noise frequency and the equivalent input electron quantity.

[0014] As an optional example, under the condition of light, by placing the illuminance meter at the position of the cathode surface of the image intensifier to be tested, the illuminance of the light source is calibrated, the illuminance count values ​​of different levels are read, and the corresponding working voltage of the light source is recorded, wherein the calibrated illuminance range is 1×10 -6 lx~1×10 -1 lx.

[0015] As an optional example, the camera adopts a high-speed CMOS or CCD camera with an ISO sensitivity of 100-10000, an exposure time of 0.1ms-50ms, and an aperture size of f / 1.8 to f / 5.6.

[0016] As an optional example, during the test, the brightness gain of the image intensifier to be tested is controlled by the image intensifier controller, and the brightness gain of the image intensifier to be tested can be adjusted in the range of 5000-50000 cd / m 2 / lx.

[0017] As an optional example, the computer system is configured to quantitatively evaluate the flicker noise frequency based on the following method:

[0018] Under a certain illumination and brightness gain, based on the image data output by the camera imaging, for one of the images, calculate the global grayscale mean A, where A represents the arithmetic mean of the grayscale values ​​of all pixels in the image;

[0019] Traverse each pixel in the image, and mark the pixel with a grayscale value higher than λA as a flicker noise candidate point, where λ represents the flicker noise sensitivity coefficient;

[0020] Based on all the flicker noise candidate points, adjacent flicker noise candidate points are connected together and identified as a flicker noise event, recorded as a flicker noise point, and the flicker noise point x is determined according to the positions of all the pixels in the flicker noise point. i Size;

[0021] Identify all the flicker noise points in an image and form the flicker noise point set X of the image, X=[x1,x2,x3,...,x n ], n represents the total number of flicker noise points identified in the image;

[0022] For all images acquired within exposure time t, calculate the average number m of flicker noise points in each image;

[0023] m=(n1+n2+n3+...+nk) / k;

[0024] Where nk represents the total number of flicker noise points identified in the kth image, and k represents the number of images obtained within the exposure time t;

[0025] The flicker noise frequency f is calculated based on the total number of flicker noise points identified in all images and the exposure time:

[0026] f=m / t

[0027] Where f represents the number of flickering points that appear per second in the effective area of ​​the image intensifier under certain illumination and brightness gain, and its unit is Hz or kHz.

[0028] As an optional example, the computer system is configured to quantitatively evaluate the number of equivalent input electrons based on the following method:

[0029] The equivalent input electron number of a single flicker noise point is described by the overall brightness of the single flicker noise point, which is equivalent to the number of electrons input to the MCP input end of the image intensifier.

[0030] Then, the equivalent input electron number of a single flicker noise point is configured to be calculated based on the input illumination, the sensitivity of the cathode surface, the grayscale mean of the single flicker noise point, and the global grayscale mean. The equivalent input electron number of a single flicker noise point in an image is expressed as:

[0031] N=n'*(BA) / A*(L×S×s×t / e);

[0032] Where n' represents the number of pixels in a single flicker noise point in the image, B represents the grayscale mean of all pixels in a single flicker noise point, A represents the global grayscale mean of the image, L represents the illumination, S represents the sensitivity of the cathode surface, s represents the pixel area of ​​a single flicker noise point, t represents the exposure time, and e represents the electron charge.

[0033] As an optional example, the computer system is configured to obtain an equivalent input electron number distribution of the entire image based on the equivalent input electron number of a single flicker noise point, which is used to describe the different brightness distributions of the flicker noise points in the entire image, and thereby obtain an average equivalent input electron number N' of the entire image, which is expressed as:

[0034] N'=the sum of the equivalent input electron numbers of all flicker noise points in the image / the total number of flicker noise points in the image.

[0035] According to a second aspect of the present invention, a method for quantitatively evaluating flicker noise of an image intensifier based on the aforementioned quantitative evaluation system is also provided, comprising the following steps:

[0036] Step 1. Calibrate the illuminance at the cathode surface of the image intensifier to be tested: Place a standard illuminometer at the cathode surface of the image intensifier to be tested, adjust the luminous intensity of the light source by controlling the power supply voltage of the light source, read the illuminance count value, and record the corresponding operating voltage of the light source;

[0037] Step 2: Place the image intensifier to be tested in the test position in the test darkroom and connect the power supply and image intensifier controller circuits. Under lighting conditions, adjust the focus of the camera so that it can clearly image the fluorescent surface of the image intensifier to be tested, and fix the position of the camera.

[0038] Step 3: Close the test dark box and control the operating voltage of the light source through the light source controller so that the illuminance provided in the test dark box reaches the required test illuminance L;

[0039] Control the power supply voltage of the image intensifier to be tested so that it works at a certain test brightness gain;

[0040] Step 4: The main controller controls the camera to capture images, collects a certain number of images within the exposure time t, and outputs the image data to the computer system;

[0041] Step 5: The computer system analyzes and evaluates the flicker noise points in the image, and quantitatively evaluates the flicker noise frequency, the equivalent input electron number, the distribution of the equivalent input electron number, and the average equivalent input electron number.

[0042] As an optional example, the computer system quantitatively evaluates the flicker noise frequency according to the following process:

[0043] Under a certain illumination and brightness gain, based on the image data output by the camera imaging, for one of the images, calculate the global grayscale mean A, where A represents the arithmetic mean of the grayscale values ​​of all pixels in the image;

[0044] Traverse each pixel in the image, and mark the pixel with a grayscale value higher than λA as a flicker noise candidate point, where λ represents the flicker noise sensitivity coefficient;

[0045] Based on all the flicker noise candidate points, adjacent flicker noise candidate points are connected together and identified as a flicker noise event, recorded as a flicker noise point, and the flicker noise point x is determined according to the positions of all the pixels in the flicker noise point. i Size;

[0046] Identify all the flicker noise points in an image and form the flicker noise point set X of the image, X=[x1,x2,x3,...,x n ], n represents the total number of flicker noise points identified in the image;

[0047] For all images acquired within exposure time t, calculate the average number m of flicker noise points in each image;

[0048] m=(n1+n2+n3+...+nk) / k;

[0049] Where nk represents the total number of flicker noise points identified in the kth image, and k represents the number of images obtained within the exposure time t;

[0050] The flicker noise frequency f is calculated based on the total number of flicker noise points identified in all images and the exposure time:

[0051] f=m / t

[0052] Where f represents the number of flickering points that appear per second in the effective area of ​​the image intensifier under certain illumination and brightness gain, and its unit is Hz or kHz.

[0053] As an optional example, the computer system quantitatively evaluates the equivalent input electron quantity according to the following process:

[0054] The equivalent input electron number of a single flicker noise point is described by the overall brightness of the single flicker noise point, which is equivalent to the number of electrons input to the MCP input end of the image intensifier.

[0055] Then, the equivalent input electron number of a single flicker noise point is configured to be calculated based on the input illumination, the sensitivity of the cathode surface, the grayscale mean of the single flicker noise point, and the global grayscale mean. The equivalent input electron number of a single flicker noise point in an image is expressed as:

[0056] N=n'*(BA) / A*(L×S×s×t / e);

[0057] Where n' represents the number of pixels in a single flicker noise point in the image, B represents the grayscale mean of all pixels in a single flicker noise point, A represents the global grayscale mean of the image, L represents the illumination, S represents the sensitivity of the cathode surface, s represents the pixel area of ​​a single flicker noise point, t represents the exposure time, and e represents the electron charge.

[0058] As an optional example, the computer system obtains the equivalent input electron number distribution of the entire image based on the equivalent input electron number of a single flicker noise point, which is used to describe the different brightness distributions of the flicker noise points in the entire image, and thereby obtains the average equivalent input electron number N' of the entire image, which is expressed as:

[0059] N'=the sum of the equivalent input electron numbers of all flicker noise points in the image / the total number of flicker noise points in the image.

[0060] It should be understood that all combinations of the foregoing concepts and the additional concepts described in more detail below, as long as such concepts are not mutually inconsistent, can be considered part of the inventive subject matter of this disclosure. In addition, all combinations of the claimed subject matter are considered part of the inventive subject matter of this disclosure.

[0061] The foregoing and other aspects, embodiments, and features of the present invention will be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the present invention, such as features and / or beneficial effects of the exemplary embodiments, will become apparent from the following description or through practice of specific embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The accompanying drawings are not intended to be drawn to scale. In the accompanying drawings, each identical or nearly identical component shown in various figures may be represented by the same reference numeral. For clarity, not every component is labeled in every figure. Embodiments of various aspects of the present invention will now be described by way of example and with reference to the accompanying drawings.

[0063] Figure 1 FIG. 4 is a schematic diagram of a flicker noise quantitative evaluation system for an image intensifier according to an exemplary embodiment of the present invention.

[0064] Figure 2 According to the present invention Figure 1 A schematic diagram of an image collected by the quantitative evaluation system of an embodiment.

[0065] Figure 3 4 is a distribution diagram of the equivalent input electron quantity of the image intensifier according to an embodiment of the present invention.

[0066] Description of reference numerals:

[0067] 1-test darkroom; 2-light source; 3-light shield; 4-image intensifier to be tested; 5-camera; 6-light source controller; 7-image intensifier controller; 8-master controller; 10-computer system. DETAILED DESCRIPTION

[0068] In order to better understand the technical content of the present invention, specific embodiments are given below in conjunction with the accompanying drawings.

[0069] Various aspects of the present invention are described in this disclosure with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. The embodiments of the present disclosure are not necessarily intended to include all aspects of the present invention. It should be understood that the various concepts and embodiments introduced above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed herein are not limited to any embodiment. In addition, some aspects of the present disclosure may be used alone or in any appropriate combination with other aspects disclosed herein.

[0070] The image intensifier flicker noise quantitative evaluation system proposed in the embodiments of the present invention is designed to test the flicker noise of the image intensifier under certain working conditions of illumination and image intensifier gain, which is manifested as snowflake noise distribution, and perform quantitative evaluation to obtain the flicker noise frequency, equivalent input electron number, equivalent input electron number distribution, and average equivalent input electron number of the image intensifier, which serve as quantitative parameters for evaluating the flicker noise number and overall brightness, thereby achieving accurate quantitative evaluation of the flicker noise of the low-light-level image intensifier and establishing an accurate test evaluation and noise quantification data foundation for suppressing the flicker noise of the low-light-level image intensifier.

[0071] Image Intensifier Flicker Noise Quantitative Evaluation System

[0072] Combine Figure 1 The image intensifier flicker noise quantitative evaluation system of the illustrated embodiment includes a test darkroom 1, a light source 2, a light shield 3, an image intensifier to be tested 4, a camera 5, a light source controller 6, an image intensifier controller 7, a master controller 8, and a computer system 10.

[0073] The image intensifier 4 to be tested is a typical PMT type image intensifier, which includes a cathode surface (i.e., photocathode) at the front end, a PMT (microchannel plate, serving as an electron multiplier) and a fluorescent surface (i.e., fluorescent screen) at the back end. The weak light signal is collected by the cathode surface, multiplied by the PMT, and then imaged on the fluorescent surface.

[0074] The image intensifier controller 7 may adopt an adjustment controller matched with the image intensifier 4 to be tested, and is generally matched with the image intensifier.

[0075] The light source controller 6 is generally a light source regulator matched with the selected light source 2 .

[0076] The main controller 8 can adopt a commercial control system and be connected to the computer system 10. The computer system 10 serves as a host computer to control the main controller 8, and the main controller 8 is used to implement adjustment and control of the light source controller 6 and the image intensifier controller 7.

[0077] Combine Figure 1 As shown, the light source 2, the light shielding plate 3, the image intensifier 4 to be tested and the camera 5 are all installed inside the test darkroom 1. The main controller 8 is connected to the image intensifier controller 7 and the light source controller 6.

[0078] The aforementioned test dark box 1 can be designed as a conventional commercial or laboratory dark box to provide a darkroom environment for testing.

[0079] The light source 2 is connected to the light source controller 6 for controlling the luminous intensity of the light source.

[0080] The image intensifier 4 to be tested is vertically installed between the light source 2 and the camera 5 along the direction of its optical center axis, with its cathode surface facing the direction of the light source 2 .

[0081] The shading plates 3 are arranged in pairs between the light source 2 and the image intensifier 4 to be tested, and the two shading plates 3 are spaced a certain distance apart so that the light beam emitted by the light source 2 passes through the gap between the two shading plates 3 and is projected onto the cathode surface of the image intensifier 4 to be tested.

[0082] The camera 5 is located at the bottom of the test darkroom 1 , with its lens facing the fluorescent surface of the image intensifier 4 to be tested, and is used to image and output the flicker noise events of the image intensifier 4 to be tested.

[0083] The computer system is connected to the camera 5, receives the image output by the camera 5, and analyzes and evaluates the flicker noise points in the image, and quantitatively evaluates the flicker noise frequency and the equivalent input electron number.

[0084] Under the condition of light, the illuminance of light source 2 is calibrated by placing the illuminance meter at the position of the cathode surface of the image intensifier to be tested, reading the illuminance count values ​​of different levels, and recording the corresponding working voltage of light source 1. The calibrated illuminance range is 1×10 -6 lx~1×10 -1 lx.

[0085] As the preferred range, during the test, the illumination range of the image intensifier flicker noise can be selected as 1×10 -5 lx~1×10 -3 lx.

[0086] The camera 5 is a high-speed CMOS or CCD camera with an ISO sensitivity of 100-10000, an exposure time of 0.1ms-50ms, and an aperture size of f / 1.8 to f / 5.6.

[0087] During the test, the gain of the image intensifier 4 to be tested is controlled by the image intensifier controller 7 .

[0088] For example, the cathode voltage, MCP voltage and screen voltage can be adjusted by the image intensifier controller 7 so that the brightness gain of the image intensifier can be adjusted in the range of 5000-50000 cd / m 2 / lx.

[0089] In an embodiment of the present invention, based on the characteristics of flicker noise, its evaluation standard is defined as the number of flicker noise points appearing per second in the effective area of ​​the image intensifier under certain illumination and brightness gain conditions of the entire tube, with the unit being Hz / kHz.

[0090] As an optional embodiment, the computer system 10 is configured to quantitatively evaluate the flicker noise frequency based on the following method:

[0091] Under a certain illumination and brightness gain, based on the image data output by the camera 5, for one of the images, calculate the global grayscale mean A, where A represents the arithmetic mean of the grayscale values ​​of all pixels in the image;

[0092] Traverse each pixel in the image, and mark the pixel with a grayscale value higher than λA as a flicker noise candidate point, where λ represents the flicker noise sensitivity coefficient;

[0093] Based on all the flicker noise candidate points, adjacent flicker noise candidate points are connected together and identified as a flicker noise event, recorded as a flicker noise point, and the flicker noise point x is determined according to the positions of all the pixels in the flicker noise point. i Size;

[0094] Identify all the flicker noise points in an image and form the flicker noise point set X of the image, X=[x1,x2,x3,...,x n ], n represents the total number of flicker noise points identified in the image;

[0095] For all images acquired within exposure time t, calculate the average number m of flicker noise points in each image;

[0096] m=(n1+n2+n3+...+nk) / k;

[0097] Where nk represents the total number of flicker noise points identified in the kth image, and k represents the number of images obtained within the exposure time t;

[0098] The flicker noise frequency f is calculated based on the total number of flicker noise points identified in all images and the exposure time:

[0099] f=m / t

[0100] Where f represents the number of flickering points that appear per second in the effective area of ​​the image intensifier under certain illumination and brightness gain, and its unit is Hz or kHz.

[0101] In the embodiment of the present invention, the flicker noise sensitivity coefficient λ is used to describe the minimum sensitivity of flicker noise. That is, the lower the value of λ is, the lower the recognition degree of the snow noise selected in the recognition result is.

[0102] In the embodiment of the present invention, the value is generally between 1.4 and 1.6.

[0103] In an embodiment of the present invention, the equivalent number of input electrons of a single flicker noise point is described by the overall brightness of the single flicker noise point, which is equivalent to the number of electrons input to the MCP input end of the image intensifier. This number can be obtained by calculating the illuminance, the sensitivity of the cathode surface, the grayscale mean of the single flicker noise point and the global grayscale mean, thereby obtaining the number of electronic signals input to the equivalent MCP input end.

[0104] As an optional embodiment, the computer system 10 is configured to quantitatively evaluate the number of equivalent input electrons based on the following method:

[0105] The equivalent input electron number of a single flicker noise point is configured to be calculated based on the input illumination, the sensitivity of the cathode surface, the grayscale mean of the single flicker noise point, and the global grayscale mean. Then, the equivalent input electron number of a single flicker noise point in an image is expressed as:

[0106] N=n'*(BA) / A*(L×S×s×t / e);

[0107] Where n' represents the number of pixels in a single flicker noise point in the image, B represents the grayscale mean of all pixels in a single flicker noise point, A represents the global grayscale mean of the image, L represents the illumination, S represents the sensitivity of the cathode surface, s represents the pixel area of ​​a single flicker noise point, t represents the exposure time, and e represents the electron charge.

[0108] In embodiments of the present invention, since the intensity of each flicker noise event is not completely identical but rather has a certain distribution, the equivalent input electron number distribution is an important parameter for describing the flicker noise characteristics of the image intensifier under fixed operating conditions (i.e., test conditions). Taking the test results of an image intensifier as an example, the distribution of multiple flicker noise events exhibits randomness, i.e., snowflake noise exhibits different distribution characteristics of bright and dark colors. The equivalent input electron number of a single flicker noise point is used to describe the bright and dark characteristics of different snowflake noise points, and the equivalent input electron number distribution is used to intuitively describe the differences in brightness distribution corresponding to different snowflake noise points. Therefore, using the equivalent input electron number, the equivalent input electron number distribution, and the average equivalent input electron number allows for a more concise and intuitive comparison of flicker noise characteristics.

[0109] As an optional embodiment, the computer system 10 is configured to obtain the equivalent input electron number distribution of the entire image based on the equivalent input electron number of a single flicker noise point, which is used to describe the different brightness distributions of the flicker noise points in the entire image, and thereby obtain the average equivalent input electron number N' of the entire image, which is expressed as:

[0110] N'=the sum of the equivalent input electron numbers of all flicker noise points in the image / the total number of flicker noise points in the image.

[0111] Quantitative Evaluation Method of Image Intensifier Flicker Noise

[0112] Combine Figure 1 As well as the image intensifier flicker noise quantitative evaluation system described in the above embodiments, the image intensifier flicker noise quantitative evaluation method of the present invention comprises the following steps:

[0113] Step 1: Calibrate the illuminance at the cathode surface of the image intensifier 4 to be tested: Place a standard illuminometer at the cathode surface of the image intensifier 4 to be tested, adjust the luminous intensity of the light source by controlling the supply voltage of the light source 2, read the illuminance count value, and record the corresponding operating voltage of the light source 2;

[0114] Step 2: Place the image intensifier 4 to be tested in the test position in the test darkroom 1 and connect the power supply and image intensifier controller circuits. Under lighting conditions, adjust the focal length of the camera 5 so that it can clearly image the fluorescent surface of the image intensifier 4 to be tested, and fix the position of the camera 5.

[0115] Step 3: Close the test dark box 1 and control the operating voltage of the light source 2 through the light source controller 6 so that the illuminance provided by the light source 2 in the test dark box 1 reaches the required test illuminance L;

[0116] Controlling the power supply voltage of the image intensifier 4 to be tested so that it operates at a certain test brightness gain;

[0117] Step 4: The main controller 8 controls the camera to capture images, captures a certain number of images within the exposure time t, and outputs the image data to the computer system 10;

[0118] Step 5: The computer system 10 analyzes and evaluates the flicker noise points in the image, and quantitatively evaluates the flicker noise frequency, the equivalent input electron number, the distribution of the equivalent input electron number, and the average equivalent input electron number.

[0119] In step 5, the computer system 10 quantitatively evaluates the flicker noise frequency according to the following process:

[0120] Under a certain illumination and brightness gain, based on the image data output by the camera 5, for one of the images, calculate the global grayscale mean A, where A represents the arithmetic mean of the grayscale values ​​of all pixels in the image;

[0121] Traverse each pixel in the image, and mark the pixel with a grayscale value higher than λA as a flicker noise candidate point, where λ represents the flicker noise sensitivity coefficient;

[0122] Based on all the flicker noise candidate points, adjacent flicker noise candidate points are connected together and identified as a flicker noise event, recorded as a flicker noise point, and the flicker noise point x is determined according to the positions of all the pixels in the flicker noise point. i Size;

[0123] Identify all the flicker noise points in an image and form the flicker noise point set X of the image, X=[x1,x2,x3,...,x n ], n represents the total number of flicker noise points identified in the image;

[0124] For all images acquired within exposure time t, calculate the average number m of flicker noise points in each image;

[0125] m=(n1+n2+n3+...+nk) / k;

[0126] Where nk represents the total number of flicker noise points identified in the kth image, and k represents the number of images obtained within the exposure time t;

[0127] The flicker noise frequency f is calculated based on the total number of flicker noise points identified in all images and the exposure time:

[0128] f=m / t

[0129] Where f represents the number of flickering points that appear per second in the effective area of ​​the image intensifier under certain illumination and brightness gain, and its unit is Hz or kHz.

[0130] In step 5, the computer system 10 quantitatively evaluates the equivalent input electron quantity according to the following process:

[0131] The equivalent input electron number of a single flicker noise point is described by the overall brightness of the single flicker noise point, which is equivalent to the number of electrons input to the MCP input end of the image intensifier.

[0132] Then, the equivalent input electron number of a single flicker noise point is configured to be calculated based on the input illumination, the sensitivity of the cathode surface, the grayscale mean of the single flicker noise point, and the global grayscale mean. The equivalent input electron number of a single flicker noise point in an image is expressed as:

[0133] N=n'*(BA) / A*(L×S×s×t / e);

[0134] Where n' represents the number of pixels in a single flicker noise point in the image, B represents the grayscale mean of all pixels in a single flicker noise point, A represents the global grayscale mean of the image, L represents the illumination, S represents the sensitivity of the cathode surface, s represents the pixel area of ​​a single flicker noise point, t represents the exposure time, and e represents the electron charge.

[0135] In step 5, the computer system 10 obtains the equivalent input electron number distribution of the entire image based on the equivalent input electron number of a single flicker noise point, which is used to describe the different brightness distributions of the flicker noise points in the entire image, and thereby obtains the average equivalent input electron number N' of the entire image, which is expressed as:

[0136] N'=the sum of the equivalent input electron numbers of all flicker noise points in the image / the total number of flicker noise points in the image.

[0137] Implementation Example 1

[0138] Below we combine a specific example and the attached Figure 2 、 3 The figure specifically describes an implementation process of the present invention.

[0139] Step 1: Calibrate the illuminance at the cathode surface of the image intensifier: Place a standard precision illuminometer at the position of the cathode surface of the image intensifier to be tested, adjust the luminous intensity of the light source by controlling the power supply voltage of the light source, read the illuminance count value, and record the corresponding light source working voltage. In this embodiment, the illuminance L is 2×10 -4 lx.

[0140] Step 2: Place the low-light-level image intensifier to be tested in the test position and connect all the wiring. Under lighting conditions, adjust the focus of the camera so that it can clearly image the fluorescent surface of the image intensifier, and then fix its position.

[0141] Close the darkroom and control the light source working voltage through the host computer software and the main controller so that the illumination can reach the required test illumination L. Control the power supply voltage of each channel of the image intensifier to be tested so that the image intensifier is in normal working state. Adjust the brightness gain of the image intensifier to 20000 cd / m 2 / lx.

[0142] That is, a test working condition of the image intensifier is illumination L = 2×10 -4 lx, the brightness gain of the image intensifier tube = 20000cd / m 2 / lx.

[0143] Step 3: Use the host computer software to control camera 5 to capture images. The image capture parameters are set as follows: ISO 1000, exposure time 20ms, aperture f / 5.6. The number of captured images is 10.

[0144] Step 4: After collecting the image, perform quantitative evaluation through image processing and analysis in the computer system.

[0145] Figure 2 The image shown is the result of processing one of the acquired images.

[0146] First, the global grayscale mean A of all pixels in an image is calculated. Using the global grayscale mean A as a benchmark, where A is approximately 46, all pixels with grayscale values ​​greater than 1.5 times A (i.e., 69) are recorded as candidate flicker noise points. Adjacent candidate points are connected together to identify the same flicker point event. The size of the flicker noise point is determined based on the positions of all pixels involved in the event, thereby obtaining all flicker noise points in an image.

[0147] Step 5: After processing all images collected by the same image intensifier to be tested, the flicker noise frequency and equivalent input electron number distribution of the image intensifier to be tested under the fixed test illumination and fixed whole tube gain are given.

[0148] The flicker noise frequency is defined as the number of flicker points that appear per second in the effective area of ​​the image intensifier under certain illumination and whole tube brightness gain conditions, in units of Hz or kHz.

[0149] In this embodiment, in the pictures collected with an exposure time of 20 ms, it is calculated that there are 300 flicker points in each image on average. Then, under the conditions of illumination and brightness gain, the flicker noise frequency = 300 / 20 ms = 15 kHz.

[0150] For example, in the images collected with an exposure time of 10ms, there are 100 flicker points in each image on average. Then, under the conditions of illumination and brightness gain, the flicker noise frequency = 100 / 10ms = 10kHz.

[0151] The equivalent input electron number of a single flicker noise point is defined as the overall brightness of a single flicker noise event, which is equivalent to the number of electron signals input at the MCP input end.

[0152] According to the above calculation method, Figure 2 The corresponding flicker noise events in the middle picture are calculated, and the equivalent number of input electrons obtained is N=405.

[0153] Since the intensity of each flicker noise point is not exactly the same and has a certain distribution, the equivalent input electron number distribution is an important parameter to describe the flicker noise characteristics of the image intensifier under fixed working conditions. Taking the image intensifier data of this embodiment as an example, the equivalent input electron number distribution is shown in FIG. Figure 3 , the average equivalent number of input electrons is calculated to be 208.

[0154] While the present invention has been disclosed above with reference to preferred embodiments, this is not intended to limit the present invention. Persons skilled in the art will readily appreciate that various modifications and variations can be made without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A flicker noise quantitative evaluation system for an image intensifier, characterized in that: The system comprises a test dark box (1), a light source (2), a light shield (3), an image intensifier to be tested (4), a camera (5), a light source controller (6), an image intensifier controller (7), a master controller (8) and a computer system (10); the light source (2), the light shield (3), the image intensifier to be tested (4) and the camera (5) are all installed inside the test dark box (1); the master controller (8) is connected to the image intensifier controller (7) and the light source controller (6); The test dark box (1) provides a darkroom environment for testing; The light source (2) is connected to the light source controller (6) and is used to control the luminous intensity of the light source; The image intensifier (4) to be tested is vertically installed between the light source (2) and the camera (5) along the direction of its optical center axis, with its cathode surface facing the direction of the light source (2); The light shielding plates (3) are arranged in pairs between the light source (2) and the image intensifier (4) to be tested, and the two light shielding plates (3) are spaced a certain distance apart, so that the light beam emitted by the light source (2) passes through the space between the two light shielding plates (3) and is projected onto the cathode surface of the image intensifier (4) to be tested; The camera (5) is located at the bottom of the test dark box (1), with its lens facing the fluorescent surface of the image intensifier (4) to be tested, and is used to image and output the flicker noise event of the image intensifier (4) to be tested; The computer system is connected to the camera (5), receives the image output by the camera (5), and analyzes and evaluates the flicker noise points in the image, quantitatively evaluating the flicker noise frequency and the equivalent input electron quantity.

2. The image intensifier flicker noise quantitative evaluation system according to claim 1, characterized in that: Under the condition of light, the illuminance of the light source (2) is calibrated by placing the illuminance meter at the position of the cathode surface of the image intensifier to be tested, reading the illuminance count values ​​of different levels, and recording the corresponding working voltage of the light source (1), wherein the calibrated illuminance range is 1×10 -6 lx~1×10 -1 lx.

3. The image intensifier flicker noise quantitative evaluation system according to claim 1, characterized in that: The camera (5) adopts a high-speed CMOS or CCD camera with a sensitivity ISO of 100-10000, an exposure time of 0.1ms-50ms, and an aperture size of f / 1.8 to f / 5.

6.

4. The image intensifier flicker noise quantitative evaluation system according to claim 1, characterized in that: During the test, the image intensifier controller (7) controls the gain of the image intensifier (4) to be tested, and the brightness gain of the image intensifier (4) to be tested can be adjusted in the range of 5000-50000 cd / m 2 / lx.

5. The image intensifier flicker noise quantitative evaluation system according to claim 1, characterized in that: The computer system (10) is configured to quantitatively evaluate the flicker noise frequency based on the following method: Under a certain illumination and brightness gain, based on the image data output by the camera (5), for one of the images, a global grayscale mean A is calculated, where A represents the arithmetic mean of the grayscale values ​​of all pixels in the image; Traverse each pixel in the image, and mark the pixel with a grayscale value higher than λA as a flicker noise candidate point, where λ represents the flicker noise sensitivity coefficient; Based on all the flicker noise candidate points, adjacent flicker noise candidate points are connected together and identified as a flicker noise event, recorded as a flicker noise point, and the flicker noise point x is determined according to the positions of all the pixels in the flicker noise point. i Size; Identify all the flicker noise points in an image and form the flicker noise point set X of the image, X=[x1,x2,x3,...,x n ], n represents the total number of flicker noise points identified in the image; For all images acquired within exposure time t, calculate the average number m of flicker noise points in each image; m=(n1+n2+n3+...+nk) / k; Where nk represents the total number of flicker noise points identified in the kth image, and k represents the number of images obtained within the exposure time t; The flicker noise frequency f is calculated based on the total number of flicker noise points identified in all images and the exposure time: f=m / t Where f represents the number of flickering points that appear per second in the effective area of ​​the image intensifier under certain illumination and brightness gain, and its unit is Hz or kHz.

6. The image intensifier flicker noise quantitative evaluation system according to claim 5, characterized in that: The computer system (10) is configured to quantitatively evaluate the number of equivalent input electrons based on the following method: The equivalent input electron number of a single flicker noise point is described by the overall brightness of the single flicker noise point, which is equivalent to the number of electrons input to the MCP input end of the image intensifier. Then, the equivalent input electron number of a single flicker noise point is configured to be calculated based on the input illumination, the sensitivity of the cathode surface, the grayscale mean of the single flicker noise point, and the global grayscale mean. The equivalent input electron number of a single flicker noise point in an image is expressed as: N=n'*(BA) / A*(L×S×s×t / e); Where n' represents the number of pixels in a single flicker noise point in the image, B represents the grayscale mean of all pixels in a single flicker noise point, A represents the global grayscale mean of the image, L represents the illumination, S represents the sensitivity of the cathode surface, s represents the pixel area of ​​a single flicker noise point, t represents the exposure time, and e represents the electron charge.

7. The image intensifier flicker noise quantitative evaluation system according to claim 6, characterized in that: The computer system (10) is configured to obtain the equivalent input electron number distribution of the entire image based on the equivalent input electron number of a single flicker noise point, which is used to describe the different brightness distributions of the flicker noise points in the entire image, and thereby obtain the average equivalent input electron number N' of the entire image, which is expressed as: N'=the sum of the equivalent input electron numbers of all flicker noise points in the image / the total number of flicker noise points in the image.

8. A method for quantitatively evaluating image intensifier flicker noise of the image intensifier flicker noise quantitative evaluation system according to claim 1, characterized in that: The following steps are involved: Step 1, calibrating the illuminance at the position of the cathode surface of the image intensifier (4) to be tested: placing a standard illuminometer at the position where the cathode surface of the image intensifier (4) to be tested is located, adjusting the luminous intensity of the light source by controlling the power supply voltage of the light source (2), reading the illuminance count value, and recording the corresponding operating voltage of the light source (2); Step 2: Place the image intensifier (4) to be tested in the test position in the test dark box (1) and connect the power supply and the image intensifier controller circuit. Under the condition of light, adjust the focal length of the camera (5) so that it can clearly image the fluorescent surface of the image intensifier (4) to be tested, and fix the position of the camera (5); Step 3: close the test dark box (1), and control the operating voltage of the light source (2) through the light source controller (6) so that the illuminance provided by the light source (2) in the test dark box (1) reaches the required test illuminance L; Controlling the power supply voltage of the image intensifier (4) to be tested so that it operates at a certain test brightness gain; Step 4: Control the camera to capture images through the main controller (8), capture a certain number of images within the exposure time t, and output the image data to the computer system (10); Step 5: The computer system (10) analyzes and evaluates the flicker noise points in the image, and quantitatively evaluates the flicker noise frequency, the equivalent input electron number, the distribution of the equivalent input electron number, and the average equivalent input electron number.

9. The method for quantitatively evaluating flicker noise of an image intensifier according to claim 8, wherein: The computer system (10) quantitatively evaluates the flicker noise frequency according to the following process: Under a certain illumination and brightness gain, based on the image data output by the camera (5), for one of the images, a global grayscale mean A is calculated, where A represents the arithmetic mean of the grayscale values ​​of all pixels in the image; Traverse each pixel in the image, and mark the pixel with a grayscale value higher than λA as a flicker noise candidate point, where λ represents the flicker noise sensitivity coefficient; Based on all the flicker noise candidate points, adjacent flicker noise candidate points are connected together and identified as a flicker noise event, recorded as a flicker noise point, and the flicker noise point x is determined according to the positions of all the pixels in the flicker noise point. i Size; Identify all the flicker noise points in an image and form the flicker noise point set X of the image, X=[x1,x2,x3,...,x n ], n represents the total number of flicker noise points identified in the image; For all images acquired within exposure time t, calculate the average number m of flicker noise points in each image; m=(n1+n2+n3+...+nk) / k; Where nk represents the total number of flicker noise points identified in the kth image, and k represents the number of images obtained within the exposure time t; The flicker noise frequency f is calculated based on the total number of flicker noise points identified in all images and the exposure time: f=m / t Where f represents the number of flickering points that appear per second in the effective area of ​​the image intensifier under certain illumination and brightness gain, and its unit is Hz or kHz.

10. The method for quantitatively evaluating flicker noise of an image intensifier according to claim 9, wherein: The computer system (10) quantitatively evaluates the equivalent input electron quantity according to the following process: The equivalent input electron number of a single flicker noise point is described by the overall brightness of the single flicker noise point, which is equivalent to the number of electrons input to the MCP input end of the image intensifier. Then, the equivalent input electron number of a single flicker noise point is configured to be calculated based on the input illumination, the sensitivity of the cathode surface, the grayscale mean of the single flicker noise point, and the global grayscale mean. The equivalent input electron number of a single flicker noise point in an image is expressed as: N=n'*(BA) / A*(L×S×s×t / e); Where n' represents the number of pixels in a single flicker noise point in the image, B represents the grayscale mean of all pixels in a single flicker noise point, A represents the global grayscale mean of the image, L represents the illumination, S represents the sensitivity of the cathode surface, s represents the pixel area of ​​a single flicker noise point, t represents the exposure time, and e represents the electron charge.

11. The method for quantitatively evaluating flicker noise of an image intensifier according to claim 10, wherein: The computer system (10) obtains the equivalent input electron number distribution of the entire image based on the equivalent input electron number of a single flicker noise point, which is used to describe the different brightness distributions of the flicker noise points in the entire image, and accordingly obtains the average equivalent input electron number N' of the entire image, which is expressed as: N'=the sum of the equivalent input electron numbers of all flicker noise points in the image / the total number of flicker noise points in the image.

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