CN115118887A - Automatic exposure method applied to domestic anti-radiation camera - Google Patents
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Abstract
The invention relates to an automatic exposure method applied to a domestic anti-radiation camera, which comprises the following steps: constructing a characteristic point coordinate matrix of the image; acquiring image data under different illumination conditions, selecting feature points in a frame of image according to the constructed feature point coordinate matrix, carrying out central weighted average on the feature points, and calculating a weighted average WA; WA is compared with the optimal threshold range [ TH min ,TH max ]Making a comparison if WA>TH max Indicating that the current image is an overexposed image, decreasing the exposure time if WA<TH min And indicating that the current image is an underexposed image, and increasing the exposure time. The invention can make the camera adjust the exposure time parameter according to the light change, automatically adjust the image brightness, make the image have certain self-adaptive ability, and improve the image quality.
Description
Technical Field
The invention relates to an automatic exposure method applied to a domestic anti-radiation camera, in particular to a method for processing a real-time video frame of the camera by adopting an FPGA technology and adjusting exposure parameters of the camera to achieve the aim of automatic exposure.
Background
In nuclear environments such as nuclear power stations, equipment needs to be monitored, and normal operation of instruments and equipment is guaranteed. However, the conventional camera cannot withstand high doses of nuclear radiation due to the high nuclear radiation in the nuclear environment. Therefore, it is necessary to equip a dedicated radiation-resistant camera in a nuclear environment. The foreign radiation-resistant camera is expensive and not beneficial to the mass application in the environment of nuclear power stations and the like. In view of this demand, domestic radiation-resistant cameras have come into force. The unit such as the institute of photoelectric technology of the Chinese academy of sciences, the institute of microelectronic technology of Beijing, and the like carries out the research on the localization of the radiation-resistant camera. However, due to the limitation of technical conditions, the domestic radiation-resistant device has a large difference in storage capacity and calculation speed from the non-radiation-resistant device, and cannot perform complicated calculation.
When the lighting conditions change, the camera needs to be exposed in order to see the image clearly. If the exposure is excessive, the whole image is bright, and some high-brightness information is possibly lost; if the exposure is insufficient, the whole image is dark, and the detail information of the object is easily invisible. In a nuclear environment, manual exposure adjustment cannot be realized, and the requirement of exposure of the camera can be met by an automatic exposure technology without manual intervention.
Most of the traditional automatic exposure methods adopt a gray level average value method, the gray level average value is calculated for the whole image, and the exposure parameters of the camera are adjusted by using the gray level average value as a threshold value. However, the gray-scale average method only reflects the overall average value of the image, and when the difference between the target and the background of the image is large, the image cannot be correctly exposed. There are also algorithms, such as using histogram and information entropy to calculate the threshold of the image, or using artificial intelligence based methods, which are complex, have high requirements on hardware, have low processing speed, and are difficult to apply on a platform with limited resources.
The nuclear power station area position is relatively fixed, the image type is single, and when an automatic exposure algorithm is designed, images of different scenes and different types do not need to be collected, and only images with different brightness in the nuclear power station environment need to be collected. According to the observation habit of human eyes, the method sets the central area of the image as a key area, applies higher weight to the central area by an automatic exposure algorithm, sets the peripheral area of the image as a non-key area, and applies lower weight to the peripheral area by the automatic exposure algorithm.
In order to ensure that an automatic exposure algorithm is only sensitive to light change and is not sensitive to objects which are suddenly flashed, such as people who suddenly walk, high-energy light beams generated when equipment is piled up and stopped, or the situation that light and light are alternately turned on and off due to poor contact of light and the like, if the conditions are exposed, images flicker all the time, and the images cannot be correctly displayed. In order to correctly display the image, the method adopts a delay exposure strategy, namely a fixed time interval t is set, one frame of image is collected at intervals of t, after three frames of images are obtained, if at least two frames of the three frames of images are images exceeding a threshold value, an automatic exposure process is started, and otherwise, the automatic exposure process is not started.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method is based on a central weighted average algorithm, the designed algorithm can be realized on an FPGA (field programmable gate array), and the problems that the existing automatic exposure method has high utilization rate of hardware resources, high time cost and difficult real-time application on a platform with limited resources are solved.
The technical scheme of the invention is as follows: an automatic exposure method applied to a domestic anti-radiation camera comprises the following steps:
(1) the camera starts an automatic exposure function;
(2) selecting representative M points X N points in a frame of image to replace the whole image, and selecting coordinates of the M points X N points to construct an image characteristic point coordinate matrix I (M, N);
(3) for any time T 1 Calculating T 1 Time, T 1 + T time, T 1 The central key weighted average WA of the pixel points corresponding to the input image feature point coordinate matrix I (M, N) at the moment of +2t 1 、WA 2 、WA 3 (ii) a t represents a specified time interval;
(4) determining WA 1 、WA 2 、WA 3 Whether in the optimal threshold range TH min ,TH max ]If more than two of the three images exceed the optimal threshold range, the current image is not the normally exposed image, the step (5) is carried out, and an automatic exposure process is executed; otherwise, the current image is the image with normal exposure, no processing is carried out, and the step (3) is returned;
(5) calculating the central key weighted average WA of the input image at the current moment, and acquiring the exposure time ET currently set by the camera i ；
(6) According to WA 1 、WA 2 、WA 3 And exposure time ET i And controlling the camera to increase the exposure time or decrease the exposure time to realize automatic exposure.
In the step (6), the specific determination process for increasing the exposure time is as follows: if WA<TH min And ET i <ET max If the camera is under-exposed, the exposure time is increased to enable the exposure time ET at the next moment i+1 ＝ET i + L, wherein ET max The maximum value of the exposure time supported by the camera is obtained, L is the step length of the change of the exposure time, and after the execution is finished, the step (5) is returned; otherwise, go to step (7).
In the step (6), the specific determination process for reducing the exposure time is as follows: (7) if WA>TH max And ET i >ET min If the camera is overexposed, the exposure time is reduced, and the exposure time ET at the next moment is made i+1 ＝ET i -L, wherein ET min The minimum value of the exposure time supported by the camera is obtained, L is the step length of the change of the exposure time, and after the execution is finished, the step (5) is returned; otherwise, returning to the step (3).
The step length
Wherein g is the number of steps for which the exposure time is adjustable.
And (2) the camera in the step (1) adopts a radiation-resistant camera, and the exposure time is controlled through upper computer software and FPGA software.
And (2) the camera in the step (1) is controlled by software, and an automatic exposure function is started by default after power-on initialization.
And (2) controlling the opening and closing of the automatic exposure function by the camera in the step (1) through upper computer software, executing manual exposure through the upper computer software when the automatic exposure function is closed, and enabling the manual exposure function of the upper computer software to be ineffective when the automatic exposure function is opened.
The central weighted average algorithm in the step (3) means that more weight is applied to a central area concerned by human eyes and less weight is applied to a peripheral area which is not concerned by the human eyes when weighted average is carried out on an input frame of image.
The specific process of the step (3) is as follows: divide the image into a central area A 1 And the surrounding area A 2 Based on the designed central weighted average algorithm, according to the formula WA ═ λ 1 *A 1 +λ 2 *A 2 Separately calculating T 1 Time, T 1 + T time, T 1 The central key weighted average WA of the pixel points corresponding to the input image feature point coordinate matrix I (M, N) at the moment of +2t 1 、WA 2 、WA 3 (ii) a Wherein λ is 1 、λ 2 Are weighting coefficients.
The weighting coefficient lambda 1 Greater than λ 2 And λ 1 +λ 2 ＝1。
Compared with the prior art, the invention has the beneficial effects that:
(1) since the human eye is always more interested in the central region of the image when viewing the image. Therefore, when the image threshold is calculated, the method applies higher weight to the central area of the image, applies lower weight to the peripheral area of the image, and performs weighted average on the image, so that the method has better exposure effect than directly averaging the image;
(2) the method selects representative characteristic points in the image to replace the whole image, and uses the characteristic points to participate in the calculation of the automatic exposure algorithm without storing the whole image, thereby greatly saving hardware resources and realizing the algorithm on an FPGA platform with limited resources.
(3) The method adopts a time-delay exposure strategy, when the calculation of an automatic exposure algorithm is carried out, the operation of 'two out of three' is adopted, namely three images with fixed time intervals are selected, the weighted average value of the three images is respectively calculated, and if more than two images exceed the optimal threshold range, the automatic exposure is carried out.
Drawings
FIG. 1 is a schematic diagram of switching between an automatic exposure mode and a manual exposure mode according to the present invention.
FIG. 2 is a flow chart of the automatic exposure mode of the present invention.
Detailed Description
The invention relates to an automatic exposure method applied to a domestic anti-radiation camera, which comprises the following steps:
(1) the camera starts an automatic exposure function;
(2) selecting representative M points X N in a frame image to replace the whole image, and selecting coordinates of the M points X N to construct an image characteristic point coordinate matrix I (M, N);
(3) for theAt any time T 1 Calculating T 1 Time, T 1 + T time, T 1 The central key weighted average WA of the pixel points corresponding to the input image feature point coordinate matrix I (M, N) at the moment of +2t 1 、WA 2 、WA 3 (ii) a t represents a specified time interval;
(4) determining WA 1 、WA 2 、WA 3 Whether it is in the optimal threshold range TH min ,TH max ]If more than two of the three images exceed the optimal threshold range, indicating that the current image is not the normally exposed image, entering the step (5) and executing an automatic exposure process; otherwise, the current image is the image with normal exposure, no processing is carried out, and the step (3) is returned;
(5) calculating the central key weighted average value WA of the input image at the current moment, and acquiring the exposure time ET currently set by the camera i ；
(6) According to WA 1 、WA 2 、WA 3 And exposure time ET i And controlling the camera to increase the exposure time or decrease the exposure time to realize automatic exposure. The specific determination process for increasing the exposure time is as follows: if WA<TH min And ET i <ET max If the camera is under-exposed, the exposure time is increased to let the exposure time ET at the next moment i+1 ＝ET i + L, wherein ET max The maximum value of the exposure time supported by the camera is obtained, L is the step length of the change of the exposure time, and after the execution is finished, the step (5) is returned; otherwise, go to step (7). The specific determination process for reducing the exposure time is as follows: (7) if WA>TH max And ET i >ET min If the camera is overexposed, the exposure time is reduced, and the exposure time ET at the next moment is made i+1 ＝ET i -L, wherein ET min The minimum value of the exposure time supported by the camera is obtained, L is the step length of the change of the exposure time, and after the execution is finished, the step (5) is returned; otherwise, returning to the step (3). The step size
Wherein g is exposure time adjustableThe number of steps.
The camera adopts an anti-radiation camera, and the exposure time is controlled through upper computer software and FPGA software. The camera is controlled by software, the automatic exposure function is started by default after power-on initialization, the starting and the closing of the automatic exposure function are controlled by the software of the upper computer, when the automatic exposure function is closed, manual exposure is executed by the software of the upper computer, and when the automatic exposure function is started, the manual exposure function of the software of the upper computer does not work.
The central weighted average algorithm is to apply more weights to a central area concerned by human eyes and less weights to peripheral areas not concerned by the human eyes when a weighted average is calculated for an input frame of image, and the specific process is as follows: divide the image into a central area A 1 And the surrounding area A 2 Based on the designed central weighted average algorithm, according to the formula WA ═ λ 1 *A 1 +λ 2 *A 2 Separately calculating T 1 Time, T 1 + T time, T 1 The central key weighted average WA of the pixel points corresponding to the input image feature point coordinate matrix I (M, N) at the moment of +2t 1 、WA 2 、WA 3 (ii) a Wherein λ is 1 、λ 2 Are weighting coefficients. Weighting factor lambda 1 Greater than λ 2 And λ of 1 +λ 2 ＝1。
The specific implementation flow of the automatic exposure method applied to the domestic radiation-resistant camera is shown in fig. 1 and fig. 2.
(1) And automatically starting the automatic exposure function after the camera is powered on for the first time, or selectively starting the automatic exposure function on the upper computer software.
(2) In order to improve the processing speed, the designed automatic exposure algorithm can run on the FPGA to construct an image characteristic point coordinate matrix I (M, N). Let M be 16, let N be 16, then M be N be 256, that is, 256 pixels in one frame of image are selected as feature points to replace the whole image. Because the PAL system image has 720 × 576 pixel points, if the selected feature points are representative, the feature points can be substituted for the whole image, and the feature points need to be uniformly distributed in the whole image, so that the feature points corresponding to the selected feature point coordinate matrix I (M, N)The coordinates are (s c, s d), wherein
(3) Of the 256 feature points, 39% of the feature points are selected, and the 100 feature points located at the center are used as the key areas, and the average value thereof is calculated
Wherein n is 1 100; selecting 61% of the points, taking 156 feature points at the periphery as non-key areas, and calculating the average value
Wherein n is 2 156. According to the formula WA ═ λ 1 *A 1 +λ 2 *A 2 Taking λ 1 0.66, take λ 2 The weighted mean of the central emphasis of the input image is calculated as 0.34. Get T 1 When t is 0 and 400ms, i.e. when 0, 400ms and 800ms are calculated, the central weighted average WA of the input image 1 、WA 2 、WA 3 。
(4) Determining WA 1 、WA 2 、WA 3 And (4) judging whether the exposure time is in the optimal threshold range, if more than 2 exposure times exceed the threshold range, entering the step (5) to execute the automatic exposure process, and if not, returning to the step (3).
(5) Calculating the central key weighted average value WA of a frame of image input at the current moment, and acquiring the exposure time ET currently set by the camera i 。
(6) The exposure time range supported by the known radiation-resistant camera is [ ET ] min ,ET max ]According to the formula
Calculating the step length L of automatic exposure, wherein g is the number of stages with adjustable exposure time, and if g is set to 15, namely the exposure time can be adjusted by 15 stages, calculating the step length L of automatic exposure
If WA<TH min And ET i <ET max If the camera is under-exposed, the exposure time is increased to let the exposure time ET at the next moment i+1 ＝ET i + L, return to step (5) after finishing carrying out; otherwise, go to step (7).
(7) If WA>TH max And ET i >ET min If the camera is overexposed, the exposure time is reduced, and the exposure time ET at the next moment is made i+1 ＝ET i L, after the execution is finished, returning to the step (5); otherwise, returning to the step (3).
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the present invention, and those skilled in the art can make modifications and variations of the present invention without departing from the spirit and scope of the present invention.
Claims (10)
1. An automatic exposure method applied to a domestic radiation-resistant camera is characterized by comprising the following steps:
(1) the camera starts an automatic exposure function;
(2) selecting representative M points X N in a frame image to replace the whole image, and selecting coordinates of the M points X N to construct an image characteristic point coordinate matrix I (M, N);
(3) calculating T 1 Time, T 1 + T time, T 1 The central key weighted average WA of the pixel points corresponding to the input image feature point coordinate matrix I (M, N) at the moment of +2t 1 、WA 2 、WA 3 ；T 1 Represents an arbitrary time, t represents a specified time interval;
(4) determination of WA 1 、WA 2 、WA 3 Whether it is in the optimal threshold range TH min ,TH max ]If more than two of the three images exceed the optimal threshold range, indicating that the current image is not the normally exposed image, entering the step (5) and executing an automatic exposure process; otherwise, the current image is the image with normal exposure, no processing is carried out, and the step (3) is returned;
(5) calculating the central key weighted average WA of the input image at the current moment, and acquiring the exposure time ET currently set by the camera i ；
(6) According to WA 1 、WA 2 、WA 3 And exposure time ET i And controlling the camera to increase the exposure time or decrease the exposure time to realize automatic exposure.
2. The automatic exposure method applied to the domestic radiation-resistant camera according to claim 1, characterized in that: in the step (6), the specific determination process for increasing the exposure time is as follows: if WA<TH min And ET i <ET max If the camera is under-exposed, the exposure time is increased to let the exposure time ET at the next moment i+1 ＝ET i + L, wherein ET max The maximum value of the exposure time supported by the camera is obtained, L is the step length of the change of the exposure time, and after the execution is finished, the step (5) is returned; otherwise, go to step (7).
3. The automatic exposure method applied to the domestic radiation-resistant camera according to claim 1, characterized in that: in the step (6), the specific determination process for reducing the exposure time is as follows: (7) if WA>TH max And ET i >ET min If the camera is overexposed, the exposure time is reduced, and the exposure time ET at the next moment is made i+1 ＝ET i -L, wherein ET min The minimum value of the exposure time supported by the camera is obtained, L is the step length of the change of the exposure time, and after the execution is finished, the step (5) is returned; otherwise, returning to the step (3).
4. The automatic exposure method applied to the domestic radiation-resistant camera according to claim 2 or 3, characterized in that: the step length
Wherein g is the number of steps for which the exposure time is adjustable.
5. The automatic exposure method applied to the domestic radiation-resistant camera according to claim 1, characterized in that: the camera in the step (1) adopts a radiation-resistant camera.
6. The automatic exposure method applied to the domestic radiation-resistant camera according to claim 1, characterized in that: and (2) the camera in the step (1) is controlled by software, and the automatic exposure function is started by default after power-on initialization.
7. The automatic exposure method applied to the domestic radiation-resistant camera according to claim 6, characterized in that: and (2) controlling the opening and closing of the automatic exposure function by the camera in the step (1) through upper computer software, executing manual exposure through the upper computer software when the automatic exposure function is closed, and enabling the manual exposure function of the upper computer software to be ineffective when the automatic exposure function is opened.
8. The automatic exposure method applied to the domestic radiation-resistant camera according to claim 1, characterized in that: the central weighted average algorithm in the step (3) is to apply more weight to the central region concerned by the human eyes and less weight to the peripheral region which is not concerned by the human eyes when weighted average is performed on the input frame of image.
9. The automatic exposure method applied to the domestic radiation-resistant camera according to claim 8, characterized in that: the specific process of the step (3) is as follows: divide the image into a central area A 1 And the surrounding area A 2 Based on the central emphasis of the designWeighted average algorithm, according to formula WA ═ λ 1 *A 1 +λ 2 *A 2 Separately calculating T 1 Time of day, T 1 + T time, T 1 Central key point weighted average WA of pixel points corresponding to characteristic point coordinate matrix I (M, N) of input image at +2t moment 1 、WA 2 、WA 3 (ii) a Wherein λ is 1 、λ 2 Are weighting coefficients.
10. The automatic exposure method applied to the domestic radiation-resistant camera according to claim 9, characterized in that: the weighting coefficient lambda 1 Greater than λ 2 And λ 1 +λ 2 ＝1。
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