Automatic dark level correction system

Abstract

The invention discloses a dark level automatic correction system, and relates to the technical field of image communication. The dark level automatic correction system comprises an accuracy calibration module, a uniformity calibration module and an accuracy evaluation module. Wherein the accuracy calibration module is used for dividing an image into a first area and a second area based on each area pixel of the image monitored by a camera in real time in a dark level calibration process, and carrying out image accuracy calibration according to the pixel number of the first area and each pixel value of the second area; the uniformity calibration module is used for performing pixel compensation calibration based on the uniformity evaluation result of the second region and the dark current data, and the pixel compensation calibration improves the image uniformity by dynamically adjusting a basic temperature compensation coefficient and a sensor bias voltage; and the accuracy evaluation module is used for carrying out calibration accuracy evaluation and feedback based on each region pixel of the camera image after the dark level calibration is finished.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image communication, in particular to a dark level automatic correction system. BACKGROUND

[0002] Dark level automatic correction is a core technology in the fields of image sensors and video signal processing, which is to eliminate the dark current noise caused by the physical characteristics of the device (such as semiconductor thermal excitation, manufacturing process deviation, analog circuit offset, etc.). Under the condition of no light, the sensor pixels will still generate charges due to thermal noise, forming a non-zero baseline offset. If not corrected, it will cause the dark part of the image to be gray or colored or to have fixed pattern noise, significantly reducing the signal-to-noise ratio and dynamic range.

[0003] The existing dark level automatic correction method mainly acquires the original signal value of the sensor optical black area or special dark pixels under the condition of no light, calculates the baseline offset using mathematical algorithms or dynamic models, and then corrects or clamps the effective pixel signal in real time through digital circuits or image signal processors. Some advanced systems also introduce machine learning to optimize noise modeling to eliminate the dark current interference caused by thermal noise, process deviation, etc., and finally achieve precise normalization of the dark part of the image.

[0004] For example, the patent application CN114205541A discloses an image sensor dark level correction structure and method, which includes: S1, reading the voltage values of the pixel signal and the slope signal, and inputting the comparator; S2, during the dark pixel row period, setting the reference voltage, and after the comparator is flipped twice, the output value is the CDS value of the dark pixel signal; S3, the digital module converts the output value of step S2 into a level value ΔV, and feeds back the dark level signal to the reference voltage generator; S4, during the effective pixel row period, the reference voltage is set again, and after the comparator is flipped twice, the output value is the CDS value of the effective pixel signal; S5, the digital module directly outputs the effective pixel signal of step S4 through the output module.

[0005] For example, the patent application CN104333717A discloses an algorithm and system for dark level correction of CMOS image sensors, which includes: S1, the background system sets the upper limit of the dark level and the output data offset; S2, input the image with dark level information row; S3, correct the defective pixels of the dark level information row with the median filter algorithm; S4, calculate the average dark level of each pixel channel of the dark level information row after filtering correction; S5, adjust the output data offset; S6, perform dark level correction on the input image data; S7, output the normal image. The system includes a defective pixel correction module, a correction information calculation module, and a dark level correction module. The correction information calculation module includes an average dark level calculation module and a data output offset adjustment module.

[0006] But in the process of implementing the technical scheme of the embodiment of the application, the applicant finds that the above-mentioned technology at least has the following technical problems: In the prior art, in the process of automatic dark level correction of the camera under lightless conditions, since the traditional time series model assumes that the data distribution is fixed, it cannot capture the data distribution deviation caused by short-term noise changes such as sensor temperature fluctuations and dynamic accumulation of dark current, resulting in insufficient adaptability of the model to non-stationary environments, and further causing deviation of the correction parameters from the actual dark level value, and there is a problem of low accuracy of dark level adaptive correction. SUMMARY

[0007] The embodiment of the application provides a dark level automatic correction system, solves the problem in the prior art that in the process of automatic dark level correction of the camera under lightless conditions, since the traditional time series model assumes that the data distribution is fixed, it cannot capture the data distribution deviation caused by short-term noise changes such as sensor temperature fluctuations and dynamic accumulation of dark current, resulting in insufficient adaptability of the model to non-stationary environments, and further causing deviation of the correction parameters from the actual dark level value, and there is a problem of low accuracy of dark level adaptive correction, and realizes the rapid self-adaptation of the dark level correction system to short-term noise changes under non-stationary environments.

[0008] The embodiment of the application provides a dark level automatic correction system, which comprises an accuracy calibration module, a uniformity calibration module and an accuracy evaluation module; wherein the accuracy calibration module is used for dividing an image into a first region and a second region based on the pixels of each region of the image monitored in real time by the camera in the dark level calibration process, and performing image accuracy calibration according to the number of pixels of the first region and the pixel values of the second region, the image accuracy calibration comprising dynamic adjustment of a mixing coefficient and uniformity evaluation of the second region; the uniformity calibration module is used for performing pixel compensation calibration based on the uniformity evaluation result of the second region and dark current data, the pixel compensation calibration improving the image uniformity by dynamically adjusting a basic temperature compensation coefficient and a sensor bias voltage; and the accuracy evaluation module is used for performing calibration accuracy evaluation and feedback based on the pixels of each region of the image of the camera after the dark level calibration is completed The one or more technical solutions provided in the embodiment of the application have at least the following technical effects or advantages: 1. By real-time monitoring of image pixel distribution and intelligent region division, the feature difference of high response and low response regions is accurately identified, and through dynamic adjustment of mixing coefficient and hierarchical compensation strategy, customized calibration is implemented for different region characteristics, and through combination of dark current characteristics and environmental temperature changes, multi-dimensional compensation model is established, and finally through closed loop verification mechanism and adaptive fine tuning function, complete working cycle is constructed, thereby realizing dynamic modeling of image noise distribution, intelligent suppression of global noise and precise compensation of local non-uniformity, dynamic adaptation and precise control of sensor working state, and stability and reliability of long-term system operation, so that the camera system can obtain accurate and stable dark level correction effect in various complex environments, and the imaging quality and environmental adaptability are comprehensively improved.

[0009] 2. By comparing the image pixels with the preset threshold to divide the first region and the second region, the noise proportion is calculated as the noise proportion and compared with the threshold, thereby realizing quantitative evaluation of the image noise level; the optimal mixing coefficient is obtained through the pre-set mapping relationship table and real-time noise proportion calculation, thereby constructing an adaptive filter weight adjustment system; the difference value is calculated based on the time sequence noise data and the mixing coefficient is dynamically adjusted, thereby forming a closed loop noise suppression mechanism; the gain coefficient configuration is adapted to the working mode, thereby ensuring the best correction effect in different scenes, thereby realizing precise identification and classification of image noise, intelligent filter control in dynamic environment, real-time monitoring and early warning of abnormal state, and dark level optimization correction in all scene adaptive, which significantly improves the image uniformity and system robustness.

[0010] 3. By dividing the second region into non-uniform region and uniform region and adopting differential compensation strategy, the precise identification of different response characteristic regions is realized; by dividing the dark current response curve of the non-uniform region into low illumination section and high illumination section and implementing incremental compensation and attenuation compensation, a segmented linear compensation model is established; by dynamically increasing the basic temperature compensation coefficient in the low illumination interval to suppress noise fluctuation and dynamically reducing the basic temperature compensation coefficient in the high illumination interval to optimize energy efficiency, an adaptive temperature compensation adjustment mechanism is formed; by using mean value matching and basic temperature compensation coefficient query for the uniform region, a standardized compensation process is constructed, thereby realizing fine correction of sensor non-uniformity, adaptive adjustment of compensation parameters in dynamic environment, intelligent optimization of system energy efficiency, and consistent dark level output quality in the whole region, which significantly improves the image uniformity and system adaptability. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 The structure schematic diagram of the dark level automatic correction system provided by the embodiment of the application.

[0012] Figure 2This is a flowchart of the dark level calibration process provided in an embodiment of this application. Detailed Implementation

[0013] This application provides an automatic dark level correction system, which solves the problem in the prior art where, during automatic dark level correction of cameras under no-light conditions, traditional time series models assume a fixed data distribution and cannot capture in real time the data distribution shifts caused by short-term noise changes such as sensor temperature fluctuations and dynamic accumulation of dark current. This results in insufficient adaptability of the model to non-stationary environments, leading to deviations between the correction parameters and the actual dark level values, and low accuracy of adaptive dark level correction. The overall approach is as follows: First, the image pixel distribution is analyzed in real time, and the image is intelligently divided into a first region and a second region. The temporal filtering weight is optimized by dynamically adjusting the mixing coefficient, while the uniformity of the second region is evaluated. Next, based on the uniformity evaluation results, hierarchical compensation is implemented for non-uniform regions. The bias voltage and base temperature compensation coefficient are enhanced in the low-illuminance range, and the compensation intensity is reduced in the high-illuminance range. For uniform regions, a baseline compensation combined with a smooth correction of the temperature change rate is used. Finally, the number of abnormal pixel regions after calibration is counted, triggering pixel-level fine-tuning or early warning, realizing the rapid adaptation of the dark level correction system to short-term noise changes in non-stationary environments.

[0014] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0015] like Figure 1 The diagram shows the structure of the automatic dark level correction system provided in this embodiment of the application. The automatic dark level correction system includes: an accuracy calibration module, a uniformity calibration module, and an accuracy evaluation module. The accuracy calibration module is used to divide the image into a first region and a second region based on the pixels of each region of the image obtained from real-time camera monitoring during the dark level calibration process. It then performs image accuracy calibration based on the number of pixels in the first region and the pixel values ​​in the second region. Image accuracy calibration includes dynamic adjustment of the mixing coefficient and evaluation of the uniformity of the second region. The uniformity calibration module is used to perform pixel compensation calibration based on the uniformity evaluation results of the second region and dark current data. Pixel compensation calibration improves image uniformity by dynamically adjusting the base temperature compensation coefficient and the sensor bias voltage. The accuracy evaluation module is used to evaluate and provide feedback on the calibration accuracy based on the pixels of each region of the camera image after the dark level calibration is completed.

[0016] In the embodiment, the accuracy calibration module analyzes the image pixel distribution in real time, and realizes adaptive noise suppression by dynamically adjusting the mixing coefficient and uniformity evaluation; the uniformity calibration module dynamically adjusts the basic temperature compensation coefficient and the bias voltage based on the evaluation result and the dark current data, effectively eliminating the non-uniformity of the sensor; and the accuracy evaluation module verifies the correction result at the pixel level and feeds back the optimization. The present application breaks through the limitation of traditional fixed parameters, dynamically adjusts in real time to adapt to temperature fluctuations and noise changes, and at the same time, the zoning calibration strategy significantly improves the dark field uniformity, and is especially suitable for high-precision imaging scenes.

[0017] As Figure 2 shown, a dark level calibration flowchart provided by the embodiment of the present application, in which the threshold value represents the noise proportion threshold value, the bias voltage represents the sensor bias voltage, the temperature coefficient represents the basic temperature compensation coefficient, the non-uniformity represents the non-uniform region, and the uniformity represents the uniform region. Specifically, based on the pixels of each region of the image obtained by the camera in real time during the dark level calibration process, the step of dividing the image into a first region and a second region includes: comparing the pixels of each region of the image with the preset pixel threshold value respectively, if the pixels of a certain region of the image are not lower than the preset pixel threshold value, then the pixels of the image region are recorded as the first region; if the pixels of a certain region of the image are lower than the preset pixel threshold value, then the pixels of the image region are recorded as the second region.

[0018] Among them, the step of performing image accuracy calibration according to the number of pixels of the first region and the pixel values of the second region includes: recording the ratio of the number of pixels of the first region to the total number of image pixels as the noise proportion; comparing the noise proportion with the preset noise proportion threshold value, if the noise proportion exceeds the preset noise proportion threshold value, then dynamically adjusting the mixing coefficient based on the current noise proportion and the noise proportion of the adjacent time monitoring point; if the noise proportion does not exceed the noise proportion threshold value, then judging whether the pixel value range of the second region exceeds the preset pixel range threshold value, if yes, then marking the second region as a non-uniform region; otherwise, marking the second region as a uniform region; the mixing coefficient is used to adjust the weight distribution ratio of the current frame data and the historical frame data in the time domain filtering process, so as to realize the optimal balance between image noise suppression and image quality maintenance; the time domain filtering is a technical framework in which the camera acquires continuous time sequence of image frames and utilizes the inter-frame correlation to process the image.

[0019] The time series model is that the system preprocesses historical noise ratio data, including removing outliers, interpolating missing data and smoothing noise reduction, and then combines the stability of historical data by using ADF (Augmented Dickey-Fuller Test) and KPSS (Kwiatkowski-Phillips-Schmidt-Shin Test) complementary tests. The ADF test method assumes that the sequence is non-stationary, and the KPSS test method assumes that the sequence is stationary. If the ADF test result is consistent with the assumption, the KPSS test method is further used for testing. If the ADF test result is not consistent with the assumption, it means that the historical noise ratio data is stable. If the KPSS test result is not consistent with the preset, the historical noise ratio data is stabilized by difference or transformation method. If the KPSS test result is consistent with the preset, it means that the historical noise ratio data is stable. Then, the trend, periodicity and randomness data are extracted by using the classical decomposition method, and the model order is identified by using the autocorrelation function and the partial autocorrelation function. Then, the prediction value is generated by using the maximum likelihood estimation method. Finally, the effect of the prediction value is evaluated by using the rolling window verification algorithm, and the new noise ratio data is continuously uploaded to update the model parameters, so as to ensure that the model can accurately capture the evolution law of noise.

[0020] The mixing coefficient is a weight parameter used to control the mixing of multi-frame image data, which determines the contribution proportion of the current frame and the historical frame to the output result. The larger the mixing coefficient is, the higher the weight of the current frame is, and the weaker the filtering effect is. The smaller the mixing coefficient is, the higher the weight of the historical frame is, and the stronger the noise reduction is, but it may cause motion blur. The current frame is the image frame being processed by the camera in the current detection time period, which contains the latest information of the scene, but is greatly affected by noise. In time domain filtering, the current frame usually has high weight. The historical frame is a series of old frame images captured or processed before the current frame, which provides redundant information in the time dimension and is used to suppress random noise. In a static scene, the historical frame can significantly improve the signal-to-noise ratio.

[0021] The step of dynamically adjusting the mixing coefficient based on the current noise ratio and the noise ratio of the adjacent time monitoring point includes: establishing a mapping relationship table of noise ratio and mixing coefficient in advance, which covers the mixing coefficient values corresponding to different noise ratio ranges in different camera working modes; calculating the current noise ratio in real time, and obtaining the corresponding optimal mixing coefficient from the mapping relationship table according to the current camera working mode and the noise ratio calculated in real time as an index parameter; when there is no mixing coefficient item in the mapping relationship table that matches the combination of the current noise ratio and the current camera working mode, the mixing coefficient is calculated based on the current noise ratio and the noise ratio of the adjacent time monitoring point.

[0022] The mixing coefficient calculation formula is: ; in the formula, This represents the adjusted mixing coefficient. Indicates the current mixing coefficient. The adjustment gain coefficient determines the sensitivity of the noise ratio to the adjustment of the mixing coefficient. The larger the adjustment gain coefficient, the more aggressive the response to noise changes; the smaller the adjustment gain coefficient, the more conservative the adjustment. Under static, dynamic, and extreme noise scenarios, the acquisition quality scores corresponding to different adjustment gain coefficients are tested. Based on the test data, a Gaussian process surrogate model is constructed to establish the mapping relationship between the acquisition quality score and the test data. The expected value is estimated using Monte Carlo sampling, and the adjustment gain coefficient is iteratively selected through the expected improvement function. When the image quality score improves by more than a preset score improvement threshold after five consecutive iterations, the optimal adjustment gain coefficient is output. n is the nonlinear adjustment exponent, which controls the nonlinear relationship between the noise ratio and the adjustment amount of the mixing coefficient. When n=1, it is linear adjustment; when n>1, the adjustment is more drastic in high-noise areas; when n<1, it is smooth adjustment. This can be directly derived using the Poisson-Gaussian mixed noise model. p is the noise ratio difference value.

[0023] The steps for calculating the mixing coefficient based on the current noise ratio and the noise ratio of adjacent time monitoring points include: obtaining the noise ratio difference value based on the current noise ratio and the noise ratio of adjacent time monitoring points; The mixing coefficient is dynamically adjusted based on the noise ratio difference value, the adjustment gain coefficient, and the nonlinear adjustment index; if the noise ratio still exceeds the preset noise ratio threshold after the number of consecutive adjustments exceeds the preset adjustment number threshold, a correction anomaly warning will be issued.

[0024] The noise ratio difference value is obtained as follows: the first noise ratio influence parameter is obtained by multiplying the noise ratio of the previous time monitoring point with its corresponding first weighting coefficient; the second noise ratio influence parameter is obtained by multiplying the noise ratio of the next time monitoring point with its corresponding second weighting coefficient; the first noise ratio influence parameter and the second noise ratio influence parameter are coupled to obtain the noise ratio influence parameter; the absolute difference between the current noise ratio and the noise ratio influence parameter is recorded as the noise ratio difference value.

[0025] The gain adjustment factor is dynamically configured according to the camera's operating mode, which includes static scene mode, dynamic scene mode, and extreme noise mode. The gain adjustment factor range for static scene mode is [0.8, 1.2], for dynamic scene mode it is [0.3, 0.6], and for extreme noise mode it is [1.5, 2.0].

[0026] In the present embodiment, the present application realizes accurate dark level calibration through intelligent partitioning and dynamic adjustment. Firstly, the image pixels are divided into high response (first region) and low response (second region) based on a preset threshold. Global noise adaptive control is realized through noise proportion quantitative evaluation. The limitation of traditional fixed filter coefficient is broken. If the threshold is not exceeded, local uniformity is detected through second region pixel range detection. Regional range detection can identify local aging or damage of the sensor, making temperature and bias voltage compensation more targeted. Finally, the noise suppression ratio in a static scene is improved. In a dynamic scene, the trailing effect of traditional time domain filtering is avoided. The calibration speed and accuracy far exceed traditional methods.

[0027] Further, the step of performing pixel compensation calibration based on the second region uniformity evaluation result and the dark current data comprises: if the second region is a non-uniform region, dividing the dark current response curve into a low-illumination segment and a high-illumination segment, performing bias voltage incremental compensation on the low-illumination segment based on a preset bias voltage compensation amount, and performing bias voltage attenuation compensation based on the preset bias voltage compensation amount; if the second region is a uniform region, no additional operation is performed.

[0028] The pixel compensation calibration further comprises: if the second region is a non-uniform region, in a low-illumination interval, dynamically increasing the base temperature compensation coefficient according to the fluctuation amplitude of the dark current data, and in a high-illumination interval, dynamically decreasing the base temperature compensation coefficient according to the stability of the dark current data; if the second region is a uniform region, calculating a mean value of the dark current data, and matching the mean value of the dark current data with a preset mapping table of the mean value of the dark current data-base temperature compensation coefficient corresponding relationship to obtain the base temperature compensation coefficient; in the process of smoothing the base temperature compensation coefficient based on the environmental temperature change rate, the system first monitors the change of the environmental temperature in real time by using the temperature sensor, and calculates the temperature change rate per unit time. In order to eliminate the instantaneous fluctuation noise in the temperature data, the system filters the temperature change rate by using an exponential weighted moving average algorithm. Next, the system compares the filtered temperature change rate with a preset rate correction coefficient table to obtain a dynamic correction factor. When the temperature is in the rising stage, the correction factor is positively adjusted according to the heating coefficient; when the temperature is in the falling stage, the correction factor is negatively adjusted according to the cooling coefficient, wherein the heating coefficient and the cooling coefficient are respectively set according to different temperature intervals. When the temperature is less than 0 DEG C, it is in a low-temperature section, the heating coefficient is 0.15, and the cooling coefficient is 0.10. When the temperature is in the interval (0, 40), it is in a normal temperature section, the heating coefficient is 0.12, and the cooling coefficient is 0.08. When the temperature is more than 40 DEG C, it is in a high-temperature section, the heating coefficient is 0.10, and the cooling coefficient is 0.05. The asymmetric configuration is determined by the characteristics of the silicon-based sensor. Experimental data shows that the growth rate of the dark current is about 1.5 times faster than the decay rate when the temperature rises. Then, the system multiplies the base temperature compensation coefficient by the dynamic correction factor to calculate the final compensation coefficient. In order to ensure the stability of the system, the final compensation coefficient is limited within a certain range of the base temperature compensation coefficient, so as to prevent under-compensation and over-compensation.

[0029] In the embodiment, the correction direction is opposite to the temperature change rate. In the non-uniform region, the adjustment amplitude of the bias voltage is synchronized with the dynamic change amount of the base temperature compensation coefficient, and in the uniform region, the adjustment amplitude of the bias voltage is in a fixed proportional relationship with the corrected base temperature compensation coefficient. The present application realizes accurate dark level correction by using an intelligent hierarchical compensation strategy. For the detected non-uniform region, the system intelligently divides the dark current response curve into a low-illumination section and a high-illumination section, respectively implements differential compensation, significantly improves the dark current correction accuracy, and especially improves the signal-to-noise ratio in the low-illumination section. For the uniform region, after matching the base compensation value by using the preset mapping table, the system performs smooth fine adjustment combined with the real-time temperature change rate, avoids over-correction or under-correction caused by the traditional fixed compensation value, and realizes the improvement of the dark level consistency and the reduction of the power consumption in the non-uniform region.

[0030] Further, the step of evaluating and feeding back the calibration accuracy based on the pixels of each region of the camera image after the dark level calibration comprises: counting the number of regions whose pixel values are lower than a preset pixel threshold in the camera image after the dark level calibration; if the number of regions exceeds a preset number threshold, the corresponding regions start a pixel-level fine-tuning mode, and an abnormal calibration parameter warning is issued; if the number of regions does not exceed the preset number threshold, no additional processing is performed.

[0031] In the embodiment, the pixel-level fine-tuning mode is a technical mode for fine-tuning the response of a single or local pixel of the image sensor in the dark level automatic correction system. The stable abnormal pixel is located by the inter-frame difference method, then a compensation lookup table is generated by combining the dark current reference frame and real-time data, and the target pixel is compensated by the DAC (Digital-to-Analog Converter Circuit) circuit based on the adaptive step strategy and the temperature-voltage coupling model, so as to realize the accurate calibration of the single-pixel dark level response. The closed-loop evaluation mechanism is used to ensure the final calibration quality. When the abnormal region exceeds the safety threshold, the pixel-level fine-tuning mode is triggered immediately for local accurate correction, and a parameter warning prompt is issued. If the detection result is within the tolerance range, the current calibration parameter is maintained, the objective verification of the calibration effect is realized through quantitative evaluation, and the subjective judgment error of the traditional method is avoided. The abnormal region rapid positioning and fine-tuning function can further reduce the residual non-uniformity. The warning mechanism provides a clear index for system maintenance, improves the preventive maintenance efficiency, and significantly improves the long-term stability of the dark level calibration.

[0032] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0033] The present application is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1apparatuses that carry out the functions specified in the block or blocks.

[0034] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 flow or flows and / or blocks Figure 1 the function specified in the block or blocks.

[0035] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 flow or flows and / or blocks Figure 1 the function specified in the block or blocks.

[0036] Although preferred embodiments of the application have been described herein, changes and modifications can be suggested to one skilled in the art, and it is intended that the scope of the application be limited only by the appended claims and equivalents thereof.

[0037] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. An automatic dark level correction system, characterized in that, It includes an accuracy calibration module, a uniformity calibration module, and an accuracy assessment module; The accuracy calibration module is used to divide the image into a first region and a second region based on the pixels of each region of the image obtained by the camera in real time during the dark level calibration process, and to perform image accuracy calibration according to the number of pixels in the first region and the pixel values ​​of each region in the second region. The image accuracy calibration includes dynamic adjustment of the mixing coefficient and evaluation of the uniformity of the second region. The uniformity calibration module is used to perform pixel compensation calibration based on the uniformity evaluation results of the second region and dark current data. The pixel compensation calibration improves image uniformity by dynamically adjusting the base temperature compensation coefficient and the sensor bias voltage. The accuracy evaluation module is used to evaluate and provide feedback on the calibration accuracy based on the pixels in each region of the camera image after dark level calibration.

2. The automatic dark level correction system as described in claim 1, characterized in that, The step of dividing the image into a first region and a second region based on the pixels of each region of the image obtained by real-time monitoring of the camera during the dark level calibration process includes: Each region of the image is compared with a preset pixel threshold. If the number of pixels in a certain region of the image is not lower than the preset pixel threshold, then the pixels in that region of the image are recorded as the first region. If the number of pixels in a certain region of an image is lower than a preset pixel threshold, then the pixels in that region are designated as the second region.

3. The automatic dark level correction system as described in claim 2, characterized in that, The step of calibrating image accuracy based on the number of pixels in the first region and the pixel values ​​in the second region includes: The ratio of the number of pixels in the first region to the total number of pixels in the image is denoted as the noise ratio; The noise ratio is compared with a preset noise ratio threshold. If the noise ratio exceeds the preset noise ratio threshold, the mixing coefficient is dynamically adjusted based on the current noise ratio and the noise ratio of adjacent time monitoring points. If the noise ratio does not exceed the noise ratio threshold, then determine whether the pixel value range of the second region exceeds the preset pixel range threshold. If so, mark the second region as a non-uniform region. Otherwise, mark the second region as a uniform region; The mixing coefficient is used to adjust the weight distribution ratio between the current frame data and the historical frame data during the temporal filtering process, so as to achieve an optimal balance between image noise suppression and image quality preservation. The temporal filtering is a technical framework for cameras to process images by acquiring continuous time-series image frames and utilizing inter-frame correlation.

4. The automatic dark level correction system as described in claim 3, characterized in that, The step of dynamically adjusting the mixing coefficient based on the current noise ratio and the noise ratio of adjacent time monitoring points includes: A mapping table is pre-established to correspond to the noise ratio and the mixing coefficient. The mapping table covers the mixing coefficient values ​​corresponding to different noise ratio ranges under different camera working modes. The current noise ratio is calculated in real time, and the corresponding optimal mixing coefficient is obtained from the mapping table based on the current camera working mode and the real-time calculated noise ratio is used as the index parameter. When there is no mixing coefficient item in the mapping table that matches the combination of the current noise ratio and the current camera working mode, the mixing coefficient is calculated based on the current noise ratio and the noise ratio of adjacent time monitoring points.

5. The automatic dark level correction system as described in claim 4, characterized in that, The step of calculating the mixing coefficient based on the current noise ratio and the noise ratio of adjacent time monitoring points includes: The noise ratio difference value is obtained based on the current noise ratio and the noise ratio of adjacent time monitoring points; The mixing coefficient is dynamically adjusted based on the noise ratio difference, the gain coefficient, and the nonlinear adjustment index. If the noise ratio still exceeds the preset noise ratio threshold after the number of consecutive adjustments exceeds the preset adjustment number threshold, a correction anomaly warning will be issued.

6. The automatic dark level correction system as described in claim 5, characterized in that, The method for obtaining the noise ratio difference value is as follows: The first noise ratio influence parameter is obtained based on the noise ratio of the previous time monitoring point and its corresponding first weighting coefficient. The second noise proportion influence parameter is obtained based on the noise proportion at the next time monitoring point and its corresponding second weighting coefficient. The noise ratio influence parameter is obtained by coupling the first noise ratio influence parameter and the second noise ratio influence parameter. The absolute difference between the current noise ratio and the parameter affecting the noise ratio is recorded as the noise ratio difference value.

7. The automatic dark level correction system as described in claim 5, characterized in that, The adjustment gain coefficient is dynamically configured according to the camera's operating mode, which includes static scene mode, dynamic scene mode, and extreme noise mode.

8. The automatic dark level correction system as described in claim 1, characterized in that, The step of performing pixel compensation calibration based on the uniformity evaluation results of the second region and the dark current data includes: If the second region is a non-uniform region, the dark current response curve is divided into a low-illuminance segment and a high-illuminance segment. The low-illuminance segment is compensated for by bias voltage increment based on the preset bias voltage compensation amount, and the bias voltage attenuation is compensated for based on the preset bias voltage compensation amount. If the second region is a uniform region, no additional operation is performed.

9. The automatic dark level correction system as described in claim 8, characterized in that, The pixel compensation calibration also includes: If the second region is a non-uniform region, then in the low illuminance range, the base temperature compensation coefficient is dynamically increased according to the fluctuation range of the dark current data, and in the high illuminance range, the base temperature compensation coefficient is dynamically decreased according to the stability of the dark current data. If the second region is a uniform region, the average dark current data is calculated, and the average dark current data is matched with the preset mapping table corresponding to the average dark current data and the base temperature compensation coefficient. The base temperature compensation coefficient is then smoothed and corrected based on the rate of change of ambient temperature.

10. The automatic dark level correction system as described in claim 1, characterized in that, The steps for evaluating and providing feedback on the calibration accuracy of pixels in each region of the camera image after dark level calibration include: Count the number of regions in the camera image whose pixel values ​​are lower than a preset pixel threshold after dark level calibration; If the number of regions exceeds the preset threshold, the corresponding region will activate the pixel-level fine-tuning mode and issue an abnormal calibration parameter warning. If the number of regions does not exceed the preset threshold, no additional processing will be performed.

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