Image optimization method and device of ultraviolet sensor, terminal equipment and storage medium

Abstract

The invention discloses an image optimization method and device for an ultraviolet sensor, terminal equipment and a storage medium, and belongs to the technical field of photoelectric signal processing, and the method comprises the steps: obtaining a plurality of historical dark current noise measurement values, historical chip temperatures, current original electric signals and current chip temperatures of the ultraviolet sensor; then according to the data, a parameter optimization model is constructed by taking dark current prediction error minimization as a target, a current pixel dark current initial value, a current sensitivity coefficient and a current dark charge accumulation time constant are solved, then a dark current exponential decay model is constructed, and a dark current noise prediction value at the current integration time is solved; removing the dark current noise predicted value from the original electric signal to generate a current original ultraviolet image; and finally, performing noise restoration on the current original ultraviolet image to obtain an optimized ultraviolet image. By implementing the invention, the problem that the quality of the generated ultraviolet image is reduced can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of photoelectric signal processing, and in particular to an image optimization method, device, terminal equipment and storage medium for an ultraviolet sensor. Background Art

[0002] Ultraviolet imaging technology has important applications in space exploration, biomedicine, industrial inspection, and other fields. When operating in low illumination, CCD (charge-coupled device) UV sensors generate dark current noise. Therefore, algorithms are often required to remove this dark current noise when generating the final UV image.

[0003] Dark current noise exhibits significant temperature-time coupling characteristics, and its spatiotemporal non-uniformity far exceeds that of the visible light band. Existing correction algorithms rely on periodic reference dark field photography. Periodic reference dark field photography involves capturing dark field images without illumination at fixed time intervals to obtain dark current noise. However, the changes in dark current with temperature and integration time may be continuous and rapid. A fixed period makes it difficult to accurately capture the instant of these changes, and it is impossible to track the nonlinear drift of dark current with temperature and integration time in real time. As a result, the dark current noise between two shots cannot be corrected, which in turn seriously restricts the resolution capability of the UV signal, resulting in a problem of reduced quality of the resulting UV image. Summary of the Invention

[0004] The present invention provides an image optimization method, apparatus, terminal device and storage medium for an ultraviolet sensor, which can solve the problem in the prior art that the nonlinear drift of dark current with temperature and integration time cannot be tracked in real time, making it impossible to correct the dark current noise between two shots, which in turn seriously restricts the ability to analyze ultraviolet signals, resulting in a problem of reduced quality of the ultimately generated ultraviolet image.

[0005] An embodiment of the present invention provides an image optimization method for an ultraviolet sensor, comprising:

[0006] Obtaining historical dark current noise measurement values of the ultraviolet sensor at several historical preset integration times, historical chip temperatures, a current raw electrical signal generated by the ultraviolet sensor at a current integration time, and a current chip temperature;

[0007] Based on historical dark current noise measurements and historical chip temperatures, a parameter optimization model is constructed with the goal of minimizing dark current prediction error. The parameter optimization model is solved to obtain the current pixel dark current initial value, current sensitivity coefficient, and current dark charge accumulation time constant corresponding to each pixel in the pixel array of the UV sensor when the dark current prediction error is minimized.

[0008] According to the current chip temperature, the initial value of the pixel dark current, the sensitivity coefficient and the dark charge accumulation time constant, a dark current exponential decay model under the current integration time is constructed and solved to obtain the dark current noise prediction value under the current integration time.

[0009] Removing the dark current noise prediction value at the current integration time from the original electrical signal at the current integration time to obtain a current first electrical signal, and generating a current original ultraviolet image based on the current first electrical signal;

[0010] According to the pixel value of each pixel point in the current original ultraviolet image, the current original ultraviolet image is noise-repaired to obtain the current optimized ultraviolet image.

[0011] Furthermore, obtaining the current original electrical signal generated by the ultraviolet sensor at the current integration time includes:

[0012] Obtaining a first original ultraviolet image at a previous integration time, and calculating a grayscale mean value of the first original ultraviolet image based on pixel values of pixels in a preset effective area in the first original ultraviolet image;

[0013] Calculating a first integration time according to the grayscale mean, a preset maximum integration time, a preset maximum grayscale mean threshold, and a preset minimum grayscale mean threshold;

[0014] When the grayscale mean is not greater than the preset minimum grayscale mean threshold, obtaining the current original electrical signal of the ultraviolet sensor according to the preset maximum integration time;

[0015] When the grayscale mean is not less than the preset maximum grayscale mean threshold, obtaining the current original electrical signal of the ultraviolet sensor according to the preset minimum integration time;

[0016] When the grayscale mean is greater than the preset minimum grayscale mean threshold and less than the preset maximum grayscale mean threshold, a current original electrical signal of the ultraviolet sensor is acquired according to the first integration time.

[0017] Furthermore, the parameter optimization model is:

[0018]

[0019] Where I0 represents the initial value of the current pixel dark current, k represents the current sensitivity coefficient, τ represents the current dark charge accumulation time constant, N represents the total number of historical preset integration times, and I meas,i represents the i-th historical dark current noise measurement value, T i represents the i-th historical chip temperature, t iIndicates the i-th historical preset integration time.

[0020] Furthermore, before removing the dark current noise prediction value at the current integration time from the original electrical signal at the current integration time to obtain the current first electrical signal, the method further includes:

[0021] Obtain the wavelength of the ultraviolet light corresponding to the current original electrical signal;

[0022] The current original electrical signal is compensated and corrected according to the wavelength, the current original electrical signal and a preset electrical signal compensation function.

[0023] Furthermore, the noise restoration is performed on the current original ultraviolet image according to the pixel value of each pixel point in the current original ultraviolet image to obtain the current optimized ultraviolet image, including:

[0024] For each pixel in the current original ultraviolet image, a morphological opening operation is performed within a preset pixel range centered on each pixel to obtain an opening operation result image;

[0025] Calculating the difference between the current original ultraviolet image and the image resulting from the opening operation to obtain a difference image;

[0026] Calculating the pixel neighborhood mean and pixel neighborhood standard deviation corresponding to each pixel in the difference image based on the pixel values of all pixels in the preset sliding window;

[0027] A noise point determination condition is generated based on the pixel neighborhood mean and the pixel neighborhood standard deviation; wherein the noise point determination condition is:

[0028] I tophat (x,y)>μ local +5σ local

[0029] Where, I tophat (x,y) represents the pixel value of the pixel at the xth row and yth column in the difference image, μ local represents the pixel neighborhood mean, σ local represents the standard deviation of the pixel neighborhood;

[0030] The pixel points in the difference image that meet the noise judgment conditions are regarded as noise points;

[0031] In the difference image, for each noise point, the median of the pixel values of all non-noise points within a preset neighborhood centered on the noise point is used as the updated pixel value of the noise point, thereby obtaining the current optimized ultraviolet image.

[0032] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0033] The present invention provides an image optimization device for an ultraviolet sensor, comprising:

[0034] Data acquisition module, parameter optimization model solution module, dark current noise prediction module, original ultraviolet image generation module and ultraviolet image optimization module;

[0035] The data acquisition module is used to obtain historical dark current noise measurement values of the ultraviolet sensor at several historical preset integration times, historical chip temperatures, current raw electrical signals generated by the ultraviolet sensor at a current integration time, and current chip temperature;

[0036] The parameter optimization model solving module is used to construct a parameter optimization model based on historical dark current noise measurement values and historical chip temperatures with the goal of minimizing dark current prediction errors, and solve the parameter optimization model to obtain the current pixel dark current initial value, current sensitivity coefficient, and current dark charge accumulation time constant corresponding to each pixel point in the pixel array of the ultraviolet sensor when the dark current prediction error is minimized;

[0037] The dark current noise prediction module is used to construct a dark current exponential decay model under the current integration time based on the current chip temperature, the initial value of the pixel dark current, the sensitivity coefficient, and the dark charge accumulation time constant, and solve the dark current exponential decay model under the current integration time to obtain a dark current noise prediction value under the current integration time;

[0038] The original ultraviolet image generation module is used to remove the dark current noise prediction value at the current integration time from the original electrical signal at the current integration time to obtain the current first electrical signal, and generate the current original ultraviolet image according to the current first electrical signal;

[0039] The ultraviolet image optimization module is used to perform noise repair on the current original ultraviolet image according to the pixel value of each pixel point in the current original ultraviolet image to obtain the current optimized ultraviolet image.

[0040] Furthermore, the data acquisition module includes:

[0041] A grayscale mean value calculation unit, a first integration time calculation unit, a first grayscale mean value threshold value comparison unit, a second grayscale mean value threshold value comparison unit, and a third grayscale mean value threshold value comparison unit;

[0042] The grayscale mean calculation unit is used to obtain the first original ultraviolet image at the last integration time, and calculate the grayscale mean of the first original ultraviolet image based on the pixel values of the pixel points in the preset effective area in the first original ultraviolet image;

[0043] The first integration time calculation unit is configured to calculate a first integration time according to the grayscale mean, a preset maximum integration time, a preset maximum grayscale mean threshold, and a preset minimum grayscale mean threshold;

[0044] The first grayscale mean threshold comparison unit is configured to obtain the current original electrical signal of the ultraviolet sensor according to a preset maximum integration time when the grayscale mean is not greater than the preset minimum grayscale mean threshold;

[0045] The second grayscale mean threshold comparison unit is configured to obtain the current original electrical signal of the ultraviolet sensor according to a preset minimum integration time when the grayscale mean is not less than the preset maximum grayscale mean threshold;

[0046] The third grayscale mean threshold comparison unit is used to obtain the current original electrical signal of the ultraviolet sensor according to the first integration time when the grayscale mean is greater than the preset minimum grayscale mean threshold and less than the preset maximum grayscale mean threshold.

[0047] Furthermore, the parameter optimization model is constructed as follows:

[0048]

[0049] Where I0 represents the initial value of the current pixel dark current, k represents the current sensitivity coefficient, τ represents the current dark charge accumulation time constant, N represents the total number of historical preset integration times, and I meas,i represents the i-th historical dark current noise measurement value, T i represents the i-th historical chip temperature, t i Indicates the i-th historical preset integration time.

[0050] Based on the above method embodiment, the present invention provides a corresponding terminal device embodiment;

[0051] The present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the image optimization method of an ultraviolet sensor described in any embodiment of the present invention is implemented.

[0052] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0053] The present invention provides a storage medium comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the image optimization method for an ultraviolet sensor according to any embodiment of the present invention is implemented.

[0054] The embodiments of the present invention have the following beneficial effects:

[0055] The present invention provides an image optimization method, device, terminal device and storage medium for an ultraviolet sensor. The method comprises: first obtaining historical dark current noise measurement values, historical chip temperatures, current original electrical signals generated by the ultraviolet sensor at a current integration time and current chip temperature of the ultraviolet sensor at a certain historical preset integration time; then constructing a parameter optimization model based on the historical dark current noise measurement values and historical chip temperatures with the goal of minimizing dark current prediction errors, and solving the parameter optimization model to obtain the current pixel dark current initial value, current sensitivity coefficient and current sensitivity coefficient corresponding to each pixel point in the pixel array of the ultraviolet sensor when the dark current prediction error is minimized. Dark charge accumulation time constant; then, according to the current chip temperature, the initial value of the pixel dark current, the sensitivity coefficient and the dark charge accumulation time constant, a dark current exponential decay model under the current integration time is constructed, and the dark current exponential decay model under the current integration time is solved to obtain the dark current noise prediction value under the current integration time; then, the dark current noise prediction value under the current integration time is removed from the original electrical signal under the current integration time to obtain the current first electrical signal, and the current original ultraviolet image is generated according to the current first electrical signal; finally, according to the pixel value of each pixel point in the current original ultraviolet image, the current original ultraviolet image is noise-repaired to obtain the current optimized ultraviolet image. Therefore, the present invention solves the parameter optimization model through relevant historical data to obtain the current pixel dark current initial value, current sensitivity coefficient and current dark charge accumulation time constant in real time, and on this basis uses the current chip temperature, pixel dark current initial value, sensitivity coefficient and dark charge accumulation time constant to construct a dark current exponential decay model under the current integration time. That is, the present invention dynamically establishes a model that can reflect the temperature-time coupling characteristics of dark current noise, obtains the predicted value of continuous dark current noise by calculation, and realizes real-time tracking of the nonlinear drift of dark current with temperature and integration time. Finally, by deleting this dark current noise in the acquired original electrical signal in real time, the quality of the ultraviolet image is optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0057] Figure 1 The figure is a flow chart of an image optimization method for an ultraviolet sensor provided in one embodiment of the present invention.

[0058] Figure 2 It is a structural diagram of an image optimization device for an ultraviolet sensor provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0059] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0061] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0062] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0063] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0064] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0065] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0066] See also Figure 1 To address the problem in the prior art that the nonlinear drift of dark current with temperature and integration time cannot be tracked in real time, resulting in the inability to correct the dark current noise between two shots, which in turn seriously restricts the ability to resolve the ultraviolet signal and thus results in a reduction in the quality of the resulting ultraviolet image, an embodiment of the present invention provides an image optimization method for an ultraviolet sensor, comprising:

[0067] Step S101: obtaining historical dark current noise measurement values of the ultraviolet sensor at several historical preset integration times, historical chip temperatures, current raw electrical signals generated by the ultraviolet sensor at a current integration time, and current chip temperature;

[0068] Specifically, the historical dark current noise measurement values and historical chip temperatures under the most recent N preset integration times are selected; the original electrical signal is the electrical signal generated by the pixel array of the UV sensor after sensing ultraviolet light, and the historical dark current noise measurement value can be obtained through the OB (Optical Black) pixel signal.

[0069] In a preferred embodiment, obtaining the current original electrical signal generated by the ultraviolet sensor at the current integration time includes:

[0070] Obtaining a first original ultraviolet image at a previous integration time, and calculating a grayscale mean value of the first original ultraviolet image based on pixel values of pixels in a preset effective area in the first original ultraviolet image;

[0071] Specifically, based on the physical design of the CCD sensor, the coordinate range of valid pixels is pre-defined (for example, in a 1024×1024 pixel array, the range obtained after removing the pixels in 32 rows / columns at the edge). Subsequently, the pixels within this pre-defined coordinate range of valid pixels are subjected to optical black area coordination verification, and abnormal pixels with high noise or damage are regarded as invalid pixels. The area where the remaining pixels are located is the above-mentioned preset valid area.

[0072] Calculating a first integration time according to the grayscale mean, a preset maximum integration time, a preset maximum grayscale mean threshold, and a preset minimum grayscale mean threshold;

[0073] Specifically, the first integration time is calculated according to the following formula:

[0074]

[0075] Where T′ next Indicates the first integration time, T max Indicates the preset maximum integration time, μ high Indicates the preset maximum grayscale mean threshold, μ indicates the grayscale mean, μ low Indicates the preset minimum grayscale mean threshold.

[0076] When the grayscale mean is not greater than the preset minimum grayscale mean threshold, obtaining the current original electrical signal of the ultraviolet sensor according to the preset maximum integration time;

[0077] When the grayscale mean is not less than the preset maximum grayscale mean threshold, obtaining the current original electrical signal of the ultraviolet sensor according to the preset minimum integration time;

[0078] When the grayscale mean is greater than the preset minimum grayscale mean threshold and less than the preset maximum grayscale mean threshold, a current original electrical signal of the ultraviolet sensor is acquired according to the first integration time.

[0079] Specifically, the grayscale mean is used as the feedback signal, and the illumination environment interval is divided by the preset upper and lower thresholds (the preset maximum grayscale mean threshold and the preset minimum grayscale mean threshold). When μ≤μ low When μ≥μ, it is determined to be an extremely low illumination environment. At this time, the preset maximum integration time is enabled to fully accumulate the photogenerated charge. high When it is determined to be an extremely high illumination environment, in this case, it is necessary to switch to the preset minimum integration time to suppress charge overflow; in the transition interval μ low <μ<μ high When , the integration time needs to be calculated according to the inverse proportional function of the first integration time and adjusted.

[0080] Specifically, the integration time for obtaining the current original electrical signal is determined by the following formula:

[0081]

[0082] Where, T min Indicates the preset minimum integration time, T next Indicates the integration time for obtaining the current original electrical signal.

[0083] Preferably, the preset maximum integration time is 2s, the preset minimum integration time is 0.1s, the preset maximum grayscale mean threshold is 2000, and the preset minimum grayscale mean threshold is 50.

[0084] Preferably, by adjusting the integration time, the scene brightness change can be monitored in real time, and the integration time can be continuously adjusted in the range of 1ms to 500ms, which can ensure the charge collection efficiency under moonlight illumination (0.1lux) and high brightness under strong light (10 5 The exposure period is shortened to 1 / 20 of the traditional mode when the light intensity is 10 lux, thus avoiding the degradation of the modulation transfer function caused by charge overflow in the highlight area. 4 The linear response of the sensor is maintained at more than 90% within an illumination span of 1:1, which is about 3 orders of magnitude higher than that of the fixed integral mode.

[0085] In this preferred embodiment, the integration time for acquiring the current original electrical signal is determined by the first original ultraviolet image acquired at the previous integration time.

[0086] Step S102: Based on historical dark current noise measurement values and historical chip temperatures, a parameter optimization model is constructed with the goal of minimizing the dark current prediction error, and the parameter optimization model is solved to obtain the current pixel dark current initial value, current sensitivity coefficient, and current dark charge accumulation time constant corresponding to each pixel in the pixel array of the UV sensor when the dark current prediction error is minimized;

[0087] Preferably, at each preset integration time, at least 64 historical dark current noise measurement values are extracted from the optical black area.

[0088] Specifically, in order to adapt to changes in the working environment of the CCD sensor, the sliding window least squares method is used to dynamically calibrate the relevant parameters in the parameter optimization model (i.e., the current pixel dark current initial value, the current sensitivity coefficient, and the current dark charge accumulation time constant). The historical dark current noise measurement values and historical chip temperatures under the most recent N preset integration times are selected, and the above data are substituted into the parameter optimization model. The minimum solution of the parameter optimization model is solved by iteration until convergence. Among them, during the first calculation, the preset initial values are used or the above relevant parameters are obtained based on historical data. The relevant parameters are updated according to the iterative optimization results of the Levenberg-Marquardt algorithm. When new data arrives, the oldest data point in the window is removed and the new data point is added. Based on the updated window data, the iterative optimization is re-executed to obtain the latest relevant parameters mentioned above.

[0089] Specifically, the current pixel dark current initial value, current sensitivity coefficient and current dark charge accumulation time constant corresponding to each pixel point in the pixel array of the ultraviolet sensor are obtained through a preset spatial position index.

[0090] In a preferred embodiment, the parameter optimization model is:

[0091]

[0092] Where I0 represents the initial value of pixel dark current, k represents the sensitivity coefficient, τ represents the dark charge accumulation time constant, N represents the total number of historical preset integration times, and I meas,i represents the i-th historical dark current noise measurement value, T i represents the i-th historical chip temperature, t i Indicates the i-th historical preset integration time.

[0093] Preferably, in this case, if the chip temperature suddenly rises, this method can update the chip temperature sensitivity coefficient and the initial pixel dark current value to obtain the parameter value after the temperature rises. After long-term use, the initial pixel dark current value may slowly increase due to device aging, and the sliding window mechanism can continuously track and correct the parameter.

[0094] Preferably, experiments show that this method can control the residual error of dark current noise to 0.3e in the temperature range of -30℃ to 50℃. - / pixel or less, the accuracy is improved by about 8 times compared with the traditional fixed parameter subtraction method.

[0095] Step S103: constructing a dark current exponential decay model at the current integration time based on the current chip temperature, the initial value of the pixel dark current, the sensitivity coefficient, and the dark charge accumulation time constant, and solving the dark current exponential decay model at the current integration time to obtain a dark current noise prediction value at the current integration time;

[0096] Specifically, in dark current noise processing, the physical properties of the optical black area (OB pixel) of the CCD sensor are fully utilized. Since the OB pixel and the effective pixel array use the same manufacturing process and are completely shielded from light, their output signal consists only of dark current noise and thermal noise, providing an ideal calibration benchmark for dark current modeling. Based on the spatiotemporal statistical characteristics of the OB pixel, a dark current exponential decay model is established. The dark current exponential decay model is:

[0097] I dark (t,y,t)=I′0(x,y)·e k′(x,y)T′(x,y) ·(1-e -t / τ′(x,y) )

[0098] Where, I dark(x, y, t) represents the predicted dark current noise value at the pixel in the x-th row and y-th column at the current integration time t, T′(x, y) represents the chip temperature at the pixel in the x-th row and y-th column, k′(x, y) represents the current sensitivity coefficient at the pixel in the x-th row and y-th column, and I′0(x, y) represents the initial dark current value of the current pixel in the x-th row and y-th column.

[0099] Step S104: removing the dark current noise prediction value at the current integration time from the original electrical signal at the current integration time to obtain a current first electrical signal, and generating a current original ultraviolet image according to the current first electrical signal;

[0100] Specifically, after obtaining the dark current noise prediction value for the current integration time, it is subtracted pixel by pixel from the original electrical signal for the current integration time. This method can achieve a stable dark current suppression rate of over 95%. After obtaining the current first electrical signal, the first electrical signal can be converted into the original ultraviolet image through a series of processing steps including photoelectric conversion, signal amplification, filtering and noise reduction, analog-to-digital conversion, image correction and enhancement.

[0101] Preferably, in the above-mentioned signal amplification stage, the low illumination area, the medium illumination area and the high illumination area are divided according to the size of the pixel value of the pixel point in the pixel array of the current first electrical signal, and the signal amplification is achieved according to different gain sizes according to the following formula:

[0102]

[0103] In the formula, G(V) represents the variation of gain G in the UV imaging system with the input signal intensity V, G analog Indicates the analog gain coefficient in low illumination area (weak signal condition), G digital Indicates the digital gain parameters of the medium illumination area and the high illumination area, V max Indicates the maximum allowable input signal strength of the sensor (saturation threshold).

[0104] Preferably, by adopting the above-mentioned gain adjustment strategy, a Sigmoid function is used at the boundary of the illumination partition to realize gain gradual change, thereby avoiding step noise and achieving transition band smoothing.

[0105] Specifically, for low-illuminance areas, a hybrid amplification mode of analog gain (×32) and digital gain (×8) is enabled: the analog gain pre-stage can effectively suppress the noise of subsequent circuits, and by configuring the feedback resistor network of the programmable transimpedance amplifier (TIA), the input reference noise voltage is reduced to 0.8μV while increasing the signal amplitude; the digital gain post-stage adopts a non-uniform quantization strategy of the 14-bit analog-to-digital converter (ADC), implementing high-density encoding in dark areas to preserve weak signal details. When the local illumination is detected to exceed the threshold ηsat When the system automatically switches to ×4 low-gain mode, it expands the full-well capacity from 20,000e- to 160,000e- by bypassing some amplifier units, and combines the dual-slope integration technology to suppress nonlinear distortion in the bright area. This gain scheduling mechanism forms a cascade optimization with the integration time control, making the CCD system -3 lx to 10 5 A dynamic range of 84dB is achieved within the 1x range, which is 12dB higher than the traditional linear gain architecture.

[0106] This algorithm resolves the inherent contradiction between weak signal sensitivity and strong light saturation resistance by optimizing the entire optoelectronic conversion process. Closed-loop integral control ensures real-time matching of the charge accumulation process with scene brightness, while intelligent gain allocation reconstructs the noise characteristics and linearity indicators of the signal transmission path. In extreme illumination scenarios such as deep space observation and night vision monitoring, this technology reduces the equivalent noise illuminance (ENL) of the UV CCD system to 2.7×10 -4 lx, while controlling the high-light recovery error within 0.8%, providing a universal solution for wide dynamic range photoelectric detection.

[0107] Preferably, in low illumination areas, the front-end analog gain × 8 is preferentially used to amplify weak photoelectric signals, and the back-end digital gain × 4 is superimposed to compensate for the signal-to-noise ratio loss caused by quantization noise. The front-end analog gain can effectively suppress the subsequent circuit noise, and the post-digital gain can implement high-density encoding in dark areas to retain weak signal details; in high illumination areas, the analog gain channel is completely closed, and the amplifier saturation distortion is avoided through pure digital gain processing, so that the linear response and detail level can be maintained when the light intensity changes drastically, realizing full-scene sensitivity enhancement from extremely weak signal detection to high dynamic range imaging.

[0108] In a preferred embodiment, before removing the dark current noise prediction value at the current integration time from the original electrical signal at the current integration time to obtain the current first electrical signal, the method further includes:

[0109] Obtain the wavelength of the ultraviolet light corresponding to the current original electrical signal;

[0110] The current original electrical signal is compensated and corrected according to the wavelength, the current original electrical signal and a preset electrical signal compensation function.

[0111] Specifically, experimental data shows that the uncompensated quantum efficiency of the CCD sensor at a wavelength of 400nm for ultraviolet light is 28%, but drops sharply to 5.7% at 250nm, showing a typical exponential decay characteristic. To describe this nonlinear response, the preset electrical signal compensation function is obtained as follows:

[0112] Sout =S in ·(1+α·e -β·λ )

[0113] Where S out Represents the original electrical signal after compensation and correction, S in Represents the current original electrical signal, λ represents the wavelength of ultraviolet light, α represents the ultraviolet absorption coefficient of the CCD sensor substrate material, and β represents the scattering attenuation coefficient of the surface passivation layer of the CCD sensor for short-wave photons.

[0114] Specifically, the researchers obtained several raw sample electrical signals and their corresponding compensated raw sample electrical signal data. By minimizing the mean square error between the two, they used the Levenberg-Marquardt optimization algorithm to determine the optimal values for the parameters α and β. At these optimal values, the theoretically calculated compensated raw electrical signals approached the actual quantum efficiency benchmark data. Results for a typical CCD sensor showed that when α = 2.37 and β = 0.018, the fitting error of the preset electrical signal compensation function in the 220-380nm band was less than 1.2%.

[0115] Specifically, a wavelength-to-pixel position mapping table is constructed by combining the filter transmission spectrum with the CCD response spectrum. The optical system is calibrated to determine the wavelength of ultraviolet light corresponding to each pixel coordinate. This compensated and corrected raw electrical signal is then calculated to apply gain compensation to the raw electrical signal on a pixel-by-pixel basis. This correction process reduces the quantum efficiency nonlinear response error in the ultraviolet band (220-280nm) from an initial 12.5% to less than 0.8%, effectively overcoming the CCD sensor's sensitivity drop in the deep ultraviolet region and meeting the stringent requirements for high-precision quantitative radiation measurement.

[0116] Preferably, by establishing a nonlinear correction model for the spectral response in the ultraviolet band, the signal distortion problem caused by the sharp drop in quantum efficiency of the CCD sensor in the short-wave region is effectively solved. The algorithm is based on the physical mechanism and material properties of the sensor's photoelectric conversion. Aiming at the characteristics of shallow penetration depth and high recombination rate of ultraviolet photons in silicon-based materials, a wavelength-dependent preset electrical signal compensation function is constructed, which significantly improves the response uniformity in the 200-400nm band. At the same time, the exponential term in the compensation function accurately matches the dependence of the silicon-based material's absorption depth (α) of ultraviolet photons on the carrier diffusion length (β). In the day-blind ultraviolet detection system, the detection sensitivity of the CCD sensor to 280nm wavelength ultraviolet light can reach 0.01 photons / second, providing a high-precision photoelectric conversion basis for applications such as atmospheric ozone layer monitoring and flame ultraviolet radiation detection.

[0117] In this preferred embodiment, the current original electrical signal is compensated and corrected by a preset electrical signal compensation function to obtain a compensated and corrected original electrical signal, and a subsequent dark current noise removal operation is performed based on the compensated and corrected original electrical signal.

[0118] Step S105: performing noise restoration on the current original ultraviolet image according to the pixel value of each pixel point in the current original ultraviolet image to obtain a current optimized ultraviolet image.

[0119] Specifically, when acquiring the original electrical signal, there will be some high-energy particle pulse noise caused by cosmic rays. For these noises, the noise is repaired by integrating the analysis method of morphological transport and local statistical characteristics.

[0120] In a preferred embodiment, performing noise repair on the current original ultraviolet image according to the pixel value of each pixel point in the current original ultraviolet image to obtain the current optimized ultraviolet image includes:

[0121] For each pixel in the current original ultraviolet image, a morphological opening operation is performed within a preset pixel range centered on each pixel to obtain an opening operation result image;

[0122] Calculating the difference between the current original ultraviolet image and the image resulting from the opening operation to obtain a difference image;

[0123] Specifically, because cosmic rays appear as isolated, bright pixels or streaks in a single-frame burst in an image, traditional frequency-domain filtering has difficulty effectively distinguishing between signal and noise. To this end, a Top-Hat transform is first used to enhance noise features: a morphological opening operation is performed on the original UV image using a 5×5 circular structuring element. The high-contrast pulse component is then extracted using the difference image between the original UV image and the opening result. The difference image is obtained using the following formula:

[0124] I tophat =I raw -γ open (I raw )

[0125] Where, I tophat Represents the difference image, I raw Represents the current original UV image, γ open Represents the morphological opening operation.

[0126] Calculating the pixel neighborhood mean and pixel neighborhood standard deviation corresponding to each pixel in the difference image based on the pixel values of all pixels in the preset sliding window;

[0127] Specifically, a dynamic threshold is set based on the statistical characteristics of the local grayscale distribution: the pixel neighborhood mean and pixel neighborhood standard deviation of each pixel are calculated using a 3×3 sliding window.

[0128] A noise point determination condition is generated based on the pixel neighborhood mean and the pixel neighborhood standard deviation; wherein the noise point determination condition is:

[0129] I tophat (x,y)>μ local +5σ local

[0130] Where, I tophat (x,y) represents the pixel value of the pixel at the xth row and yth column in the difference image, μ local represents the pixel neighborhood mean, σ local represents the standard deviation of the pixel neighborhood;

[0131] The pixel points in the difference image that meet the noise judgment conditions are regarded as noise points;

[0132] In the difference image, for each noise point, the median of the pixel values of all non-noise points within a preset neighborhood centered on the noise point is used as the updated pixel value of the noise point, thereby obtaining the current optimized ultraviolet image.

[0133] Specifically, the pixels in the difference image that meet the noise judgment conditions can be marked, and an improved weighted median restoration strategy is used to replace the pixel values of the marked pixels with the median values of the unmarked non-noise pixels in the 3×3 neighborhood.

[0134] Preferably, after updating and replacing the pixel values of noise points, edge direction detection can be simultaneously introduced to avoid loss of detail due to smoothing. Measured data shows that this method can repair 99.7% of impulse noise caused by single-particle events, while impacting the peak signal-to-noise ratio (PSNR) of weak signal targets by less than 0.2dB. Noise point repair is achieved using a 3×3 window median filter, achieving an interference removal rate of >99% while preserving image detail.

[0135] Preferably, the morphological opening operation can effectively suppress the tiny structures in the original ultraviolet image while retaining the large amplitude pulses and enhancing the local features of high contrast noise.

[0136] Optimally, dark current is modeled based on spatiotemporal separation, achieving decoupling of process deviations from environmental disturbances. The aforementioned pixel restoration strategy, combining morphology and adaptive thresholding, balances the sensitivity and specificity of impulse noise detection, overcoming the limitations of traditional single-noise reduction techniques. Therefore, in extreme radiation environments such as deep space exploration, the effective dynamic range of CCD sensors can be extended to 84dB, laying a key technical foundation for the reliable extraction of weak UV signals and achieving a step-by-step improvement in the signal-to-noise ratio (SNR) of the original signal.

[0137] In this preferred embodiment, noise restoration is performed on the current original ultraviolet image according to the pixel value of each pixel point in the current original ultraviolet image, thereby optimizing the original ultraviolet image.

[0138] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0139] like Figure 2 As shown, an embodiment of the present invention provides an image optimization device for an ultraviolet sensor, comprising:

[0140] Data acquisition module, parameter optimization model solution module, dark current noise prediction module, original ultraviolet image generation module and ultraviolet image optimization module;

[0141] The data acquisition module is used to obtain historical dark current noise measurement values of the ultraviolet sensor at several historical preset integration times, historical chip temperatures, current raw electrical signals generated by the ultraviolet sensor at a current integration time, and current chip temperature;

[0142] The parameter optimization model solving module is used to construct a parameter optimization model based on historical dark current noise measurement values and historical chip temperatures with the goal of minimizing dark current prediction errors, and solve the parameter optimization model to obtain the current pixel dark current initial value, current sensitivity coefficient, and current dark charge accumulation time constant corresponding to each pixel point in the pixel array of the ultraviolet sensor when the dark current prediction error is minimized;

[0143] The dark current noise prediction module is used to construct a dark current exponential decay model under the current integration time based on the current chip temperature, the initial value of the pixel dark current, the sensitivity coefficient, and the dark charge accumulation time constant, and solve the dark current exponential decay model under the current integration time to obtain a dark current noise prediction value under the current integration time;

[0144] The original ultraviolet image generation module is used to remove the dark current noise prediction value at the current integration time from the original electrical signal at the current integration time to obtain the current first electrical signal, and generate the current original ultraviolet image according to the current first electrical signal;

[0145] The ultraviolet image optimization module is used to perform noise repair on the current original ultraviolet image according to the pixel value of each pixel point in the current original ultraviolet image to obtain the current optimized ultraviolet image.

[0146] In a preferred embodiment, the data acquisition module includes:

[0147] A grayscale mean value calculation unit, a first integration time calculation unit, a first grayscale mean value threshold value comparison unit, a second grayscale mean value threshold value comparison unit, and a third grayscale mean value threshold value comparison unit;

[0148] The grayscale mean calculation unit is used to obtain the first original ultraviolet image at the last integration time, and calculate the grayscale mean of the first original ultraviolet image based on the pixel values of the pixel points in the preset effective area in the first original ultraviolet image;

[0149] The first integration time calculation unit is configured to calculate a first integration time according to the grayscale mean, a preset maximum integration time, a preset maximum grayscale mean threshold, and a preset minimum grayscale mean threshold;

[0150] The first grayscale mean threshold comparison unit is configured to obtain the current original electrical signal of the ultraviolet sensor according to a preset maximum integration time when the grayscale mean is not greater than the preset minimum grayscale mean threshold;

[0151] The second grayscale mean threshold comparison unit is configured to obtain the current original electrical signal of the ultraviolet sensor according to a preset minimum integration time when the grayscale mean is not less than the preset maximum grayscale mean threshold;

[0152] The third grayscale mean threshold comparison unit is used to obtain the current original electrical signal of the ultraviolet sensor according to the first integration time when the grayscale mean is greater than the preset minimum grayscale mean threshold and less than the preset maximum grayscale mean threshold.

[0153] In another preferred embodiment, the parameter optimization model is constructed as follows:

[0154]

[0155] Where I0 represents the initial value of the current pixel dark current, k represents the current sensitivity coefficient, τ represents the current dark charge accumulation time constant, N represents the total number of historical preset integration times, and I meas,i represents the i-th historical dark current noise measurement value, T i represents the i-th historical chip temperature, t i Indicates the i-th historical preset integration time.

[0156] It should be noted that the device embodiment described above is merely illustrative, wherein the modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiment provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work. The above schematic diagram is only an example of an image optimization device for an ultraviolet sensor, and does not constitute a limitation on an image optimization device for an ultraviolet sensor. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components.

[0157] Based on the above method embodiment, the present invention provides a corresponding terminal device embodiment.

[0158] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the image optimization method of the ultraviolet sensor described in any embodiment of the present invention.

[0159] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the device.

[0160] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, or a cloud server. The device may include, but is not limited to, a processor and a memory;

[0161] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the device, connecting the various parts of the device using various interfaces and lines.

[0162] The above-mentioned memory can be used to store the above-mentioned computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned device by running or executing the computer programs and / or modules stored in the above-mentioned memory, and calling the data stored in the memory. The above-mentioned memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; in addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0163] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment.

[0164] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the image optimization method of an ultraviolet sensor described in any embodiment of the present invention.

[0165] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, an executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium.

[0166] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing an image of an ultraviolet sensor, characterized in that: include: Obtaining historical dark current noise measurement values of the ultraviolet sensor at several historical preset integration times, historical chip temperatures, a current raw electrical signal generated by the ultraviolet sensor at a current integration time, and a current chip temperature; Based on historical dark current noise measurements and historical chip temperatures, a parameter optimization model is constructed with the goal of minimizing dark current prediction error. The parameter optimization model is solved to obtain the current pixel dark current initial value, current sensitivity coefficient, and current dark charge accumulation time constant corresponding to each pixel in the pixel array of the UV sensor when the dark current prediction error is minimized. According to the current chip temperature, the initial value of the pixel dark current, the sensitivity coefficient and the dark charge accumulation time constant, a dark current exponential decay model under the current integration time is constructed and solved to obtain the dark current noise prediction value under the current integration time. Removing the dark current noise prediction value at the current integration time from the original electrical signal at the current integration time to obtain a current first electrical signal, and generating a current original ultraviolet image based on the current first electrical signal; According to the pixel value of each pixel point in the current original ultraviolet image, the current original ultraviolet image is noise-repaired to obtain the current optimized ultraviolet image.

2. The image optimization method of an ultraviolet sensor according to claim 1, characterized in that: Obtaining a current original electrical signal generated by the ultraviolet sensor at a current integration time, comprising: Obtaining a first original ultraviolet image at a previous integration time, and calculating a grayscale mean value of the first original ultraviolet image based on pixel values of pixels in a preset effective area in the first original ultraviolet image; Calculating a first integration time according to the grayscale mean, a preset maximum integration time, a preset maximum grayscale mean threshold, and a preset minimum grayscale mean threshold; When the grayscale mean is not greater than the preset minimum grayscale mean threshold, obtaining the current original electrical signal of the ultraviolet sensor according to the preset maximum integration time; When the grayscale mean is not less than the preset maximum grayscale mean threshold, obtaining the current original electrical signal of the ultraviolet sensor according to the preset minimum integration time; When the grayscale mean is greater than the preset minimum grayscale mean threshold and less than the preset maximum grayscale mean threshold, a current original electrical signal of the ultraviolet sensor is acquired according to the first integration time.

3. The image optimization method of an ultraviolet sensor according to claim 2, characterized in that: The parameter optimization model is: Where I0 represents the initial value of the current pixel dark current, k represents the current sensitivity coefficient, τ represents the current dark charge accumulation time constant, N represents the total number of historical preset integration times, and I meas,i represents the i-th historical dark current noise measurement value, T i represents the i-th historical chip temperature, t i Indicates the i-th historical preset integration time.

4. The image optimization method for an ultraviolet sensor according to claim 3, characterized in that: Before removing the dark current noise prediction value at the current integration time from the original electrical signal at the current integration time to obtain the current first electrical signal, the method further includes: Obtain the wavelength of the ultraviolet light corresponding to the current original electrical signal; The current original electrical signal is compensated and corrected according to the wavelength, the current original electrical signal and a preset electrical signal compensation function.

5. The image optimization method for an ultraviolet sensor according to claim 4, characterized in that: The method of performing noise repair on the current original ultraviolet image according to the pixel value of each pixel point in the current original ultraviolet image to obtain the current optimized ultraviolet image includes: For each pixel in the current original ultraviolet image, a morphological opening operation is performed within a preset pixel range centered on each pixel to obtain an opening operation result image; Calculating the difference between the current original ultraviolet image and the image resulting from the opening operation to obtain a difference image; Calculating the pixel neighborhood mean and pixel neighborhood standard deviation corresponding to each pixel in the difference image based on the pixel values of all pixels in the preset sliding window; A noise point determination condition is generated based on the pixel neighborhood mean and the pixel neighborhood standard deviation; wherein the noise point determination condition is: I tophat (x,y)>μ local +5s local Where, I tophat (x,y) represents the pixel value of the pixel at the xth row and yth column in the difference image, μ local represents the pixel neighborhood mean, σ local represents the standard deviation of the pixel neighborhood; The pixel points in the difference image that meet the noise judgment conditions are regarded as noise points; In the difference image, for each noise point, the median of the pixel values of all non-noise points within a preset neighborhood centered on the noise point is used as the updated pixel value of the noise point, thereby obtaining the current optimized ultraviolet image.

6. An image optimization device for an ultraviolet sensor, characterized in that: include: Data acquisition module, parameter optimization model solution module, dark current noise prediction module, original ultraviolet image generation module and ultraviolet image optimization module; The data acquisition module is used to obtain historical dark current noise measurement values of the ultraviolet sensor at several historical preset integration times, historical chip temperatures, current raw electrical signals generated by the ultraviolet sensor at a current integration time, and current chip temperature; The parameter optimization model solving module is used to construct a parameter optimization model based on historical dark current noise measurement values and historical chip temperatures with the goal of minimizing dark current prediction errors, and solve the parameter optimization model to obtain the current pixel dark current initial value, current sensitivity coefficient, and current dark charge accumulation time constant corresponding to each pixel point in the pixel array of the ultraviolet sensor when the dark current prediction error is minimized; The dark current noise prediction module is used to construct a dark current exponential decay model under the current integration time based on the current chip temperature, the initial value of the pixel dark current, the sensitivity coefficient, and the dark charge accumulation time constant, and solve the dark current exponential decay model under the current integration time to obtain a dark current noise prediction value under the current integration time; The original ultraviolet image generation module is used to remove the dark current noise prediction value at the current integration time from the original electrical signal at the current integration time to obtain the current first electrical signal, and generate the current original ultraviolet image according to the current first electrical signal; The ultraviolet image optimization module is used to perform noise repair on the current original ultraviolet image according to the pixel value of each pixel point in the current original ultraviolet image to obtain the current optimized ultraviolet image.

7. The image optimization device for an ultraviolet sensor according to claim 6, characterized in that: The data acquisition module includes: A grayscale mean value calculation unit, a first integration time calculation unit, a first grayscale mean value threshold value comparison unit, a second grayscale mean value threshold value comparison unit, and a third grayscale mean value threshold value comparison unit; The grayscale mean calculation unit is used to obtain the first original ultraviolet image at the last integration time, and calculate the grayscale mean of the first original ultraviolet image based on the pixel values of the pixel points in the preset effective area in the first original ultraviolet image; The first integration time calculation unit is configured to calculate a first integration time according to the grayscale mean, a preset maximum integration time, a preset maximum grayscale mean threshold, and a preset minimum grayscale mean threshold; The first grayscale mean threshold comparison unit is configured to obtain the current original electrical signal of the ultraviolet sensor according to a preset maximum integration time when the grayscale mean is not greater than the preset minimum grayscale mean threshold; The second grayscale mean threshold comparison unit is configured to obtain the current original electrical signal of the ultraviolet sensor according to a preset minimum integration time when the grayscale mean is not less than the preset maximum grayscale mean threshold; The third grayscale mean threshold comparison unit is used to obtain the current original electrical signal of the ultraviolet sensor according to the first integration time when the grayscale mean is greater than the preset minimum grayscale mean threshold and less than the preset maximum grayscale mean threshold.

8. The image optimization device for an ultraviolet sensor according to claim 7, characterized in that: The parameter optimization model is constructed as follows: Where I0 represents the initial value of the current pixel dark current, k represents the current sensitivity coefficient, τ represents the current dark charge accumulation time constant, N represents the total number of historical preset integration times, and I meas,i represents the i-th historical dark current noise measurement value, T i represents the i-th historical chip temperature, t i Indicates the i-th historical preset integration time.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for optimizing an image of an ultraviolet sensor according to any one of claims 1 to 5 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute the image optimization method for an ultraviolet sensor according to any one of claims 1 to 5.

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