A method for suppressing vertical stripe of infrared image based on statistical characteristics

Abstract

This invention relates to the field of infrared image processing technology and proposes a method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics. The method includes: preprocessing the original grayscale image to obtain a preprocessed image; establishing temperature segmentation intervals for the preprocessed image and adaptively configuring correction parameters for the preprocessed image based on the matching results of the working environment temperature and the temperature segmentation intervals; calculating the statistical characteristic value of each column of the preprocessed image after removing outliers to calculate the global statistical mean of the preprocessed image, and calculating the column stripe deviation coefficient of the preprocessed image based on the global statistical mean and statistical characteristic values; performing temporal recursive smoothing on the column stripe deviation coefficient using the correction parameters to generate dynamic correction coefficients; and correcting the preprocessed image column by column to output the target image after vertical stripe suppression. This invention can meet the high-fidelity real-time imaging requirements in complex dynamic environments.

Description

Technical Field

[0001] This invention relates to a method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics, belonging to the field of infrared image processing technology. Background Technology

[0002] Infrared image vertical stripe dynamic suppression refers to the process of estimating and removing fixed or slowly changing vertical stripe noise from images in infrared imaging systems, caused by factors such as inconsistent detector pixel response, readout circuit noise, or scanning mechanisms. Its significance lies in the fact that this dynamic suppression effectively improves the signal-to-noise ratio and sharpness of infrared images, eliminates visual non-uniformity artifacts, and, compared to traditional single-point correction, can adapt to the slow drift of detector response characteristics, ensuring good image quality even when scene changes or system operating conditions change. This plays a crucial role in improving the reliability and accuracy of infrared imaging systems in target detection, identification, and tracking tasks.

[0003] Traditional infrared image vertical stripe suppression usually employs column grayscale mean correction based on a single frame image or a fixed two-point correction method. This method often ignores the influence of ambient temperature on the detector's response characteristics and uses globally uniform correction parameters, resulting in decreased correction accuracy and residual obvious stripe noise in wide temperature range scenarios, which cannot meet the high-fidelity real-time imaging requirements in complex dynamic environments. Summary of the Invention

[0004] This invention provides a method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics, the main purpose of which is to achieve high-fidelity real-time imaging in complex dynamic environments.

[0005] To achieve the above objectives, the present invention provides a method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics, comprising:

[0006] The original grayscale image of the target object is acquired in real time, and the operating temperature of the detector component is acquired simultaneously. The original grayscale image is preprocessed to obtain a preprocessed image.

[0007] Establish temperature segmentation intervals for the preprocessed image, and adaptively configure the correction parameters of the preprocessed image based on the matching results between the working environment temperature and the temperature segmentation intervals.

[0008] Calculate the statistical feature value of each column of the preprocessed image after removing outliers, so as to calculate the global statistical mean of the preprocessed image, and calculate the column stripe deviation coefficient of the preprocessed image based on the global statistical mean and the statistical feature value.

[0009] The correction parameters are used to perform time-domain recursive smoothing on the column stripe deviation coefficient to generate dynamic correction coefficients;

[0010] By using the dynamic correction coefficients and a preset gradient threshold, the preprocessed image is corrected column by column to obtain the target image after vertical stripe suppression.

[0011] Optionally, the preprocessed image is corrected column by column using the dynamic correction coefficients and a preset gradient threshold to obtain the target image after vertical stripe suppression, including:

[0012] Calculate the gradient magnitude of each column of pixels in the preprocessed image in the row direction;

[0013] The gradient magnitude is compared with the gradient threshold to generate a control signal for controlling the correction intensity of the preprocessed image;

[0014] By combining the control signal and the dynamic correction coefficient, the preprocessed image is corrected column by column to output the target image after vertical stripe suppression.

[0015] Optionally, the gradient magnitude is compared with the gradient threshold to generate a control signal for controlling the preprocessed image correction intensity, including:

[0016] If the gradient magnitude is greater than or equal to the gradient threshold, the texture edge region of the preprocessed image is determined to generate a first control signal for suppression correction.

[0017] If the gradient magnitude is less than the gradient threshold, a flat region of the preprocessed image is determined to generate a second control signal for performing full correction.

[0018] Based on the first control signal and the second control signal, a control signal for the preprocessed image correction intensity is generated.

[0019] Optionally, the preprocessed image is corrected column by column by combining the control signal and the dynamic correction coefficient to output the target image after vertical stripe suppression of the preprocessed image, including:

[0020] During the traversal of each column of the preprocessed image, the control signal type for each column in the preprocessed image is determined:

[0021] When the control signal type belongs to the first control signal, the current column pixels are smoothed according to the dynamic correction coefficient to obtain smoothed pixels, and the smoothed pixels are used as the first column pixels of the output image.

[0022] When the control signal type is the second control signal, the dynamic correction coefficient is used to perform column stripe correction on the current column pixels to obtain the corrected pixels, and the corrected pixels are used as the second column pixels of the output image;

[0023] After traversing each column of the preprocessed image, a target image with vertical stripe suppression of the output image is generated based on the pixels of the first column and the pixels of the second column.

[0024] Optionally, the gradient threshold is determined based on the product of the grayscale dynamic range span of the preprocessed image and a preset scaling factor.

[0025] Optionally, the original grayscale image of the target object is acquired in real time, including:

[0026] The infrared radiation of the target object is focused onto the photosensitive surface of the infrared detector to obtain the initial infrared radiation energy;

[0027] Suppressing the thermal noise of the infrared detector itself in the initial infrared radiation energy yields suppressed infrared radiation energy;

[0028] The suppressed infrared radiation energy is converted into a raw electrical signal corresponding to the temperature distribution;

[0029] The original electrical signal is subjected to image conversion processing to generate digital image data;

[0030] The digital image data is transmitted to a display device, which then generates the original grayscale image.

[0031] Optionally, the original grayscale image is preprocessed to obtain a preprocessed image, including:

[0032] The original grayscale image is subjected to defect removal processing to obtain a first processed grayscale image;

[0033] The first processed grayscale image is subjected to median filtering to obtain the preprocessed image.

[0034] Optionally, the statistical feature value of each column of the preprocessed image after removing outliers is calculated, including:

[0035] The gray values ​​of each column of pixels in the preprocessed image are sorted to obtain a pixel sequence. High gray value pixels and low gray value pixels with a preset percentage threshold are removed from the pixel sequence to obtain the target pixel sequence.

[0036] Based on the row statistical window size of the correction parameters corresponding to the preprocessed image, the range of the statistical window in the row direction is determined in the target pixel sequence, and the gray mean within the statistical window range is calculated as the statistical feature value of the current column of the preprocessed image after removing outliers.

[0037] Optionally, the column stripe deviation coefficient of the preprocessed image is calculated based on the global statistical mean and the statistical feature value, including:

[0038] The difference between the statistical feature value of each column of the preprocessed image and the global statistical mean is calculated to obtain the column response deviation;

[0039] Based on the step size of the correction coefficients of the correction parameters corresponding to the preprocessed image, a low-pass filter for the preprocessed image is constructed.

[0040] The column response deviation is input to the low-pass filter for smoothing to obtain the column stripe deviation coefficient.

[0041] Optionally, the column stripe deviation coefficient is recursively smoothed in the time domain using the correction parameters to generate dynamic correction coefficients, including:

[0042] The temporal smoothing factor of the preprocessed image is determined based on the correction parameters;

[0043] For the column stripe deviation coefficient of the current frame of the preprocessed image:

[0044] If the current frame is the starting frame, then the column stripe deviation coefficient is used as the dynamic correction coefficient of the current frame of the preprocessed image;

[0045] If the current frame is not the starting frame, the dynamic correction coefficient of the previous frame of the preprocessed image and the column stripe deviation coefficient of the current frame are weighted and summed using the temporal smoothing factor to generate the dynamic correction coefficient of the current frame of the preprocessed image.

[0046] This invention effectively solves the problem of mismatch in correction parameters caused by drastic changes in ambient temperature by establishing temperature segmentation intervals and adaptively matching correction parameters. This significantly improves the stripe suppression accuracy and stability of infrared thermal imagers in a wide temperature range environment. At the same time, by combining gradient thresholding to accurately distinguish between image texture edges and flat areas, adaptive control of correction intensity is achieved. While effectively removing stripe noise, the edge details of the image are fully preserved, avoiding edge blurring or artifacts caused by over-correction. In addition, the dynamic correction coefficients generated based on temporal recursive smoothing effectively suppress random noise and instantaneous scene interference. The impact on correction parameters ensures the continuity of dynamic correction coefficients in the time dimension, thereby significantly reducing video image flicker and jitter, and greatly improving the imaging quality and environmental adaptability of the infrared thermal imaging system. Attached Figure Description

[0047] Figure 1This is a flowchart illustrating a method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics, provided in an embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of a continuous zoom infrared thermal imager based on a statistical characteristic infrared image vertical stripe dynamic suppression method provided in an embodiment of the present invention.

[0049] Figure 3 A schematic diagram of a computer device for a method of dynamic suppression of vertical stripes in infrared images based on statistical characteristics, provided in an embodiment of the present invention;

[0050] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0051] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0052] This application provides a method for dynamically suppressing vertical stripes in infrared images based on statistical characteristics. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for dynamically suppressing vertical stripes in infrared images based on statistical characteristics can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0053] Reference Figure 1 The diagram shown is a flowchart illustrating a method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics, according to an embodiment of the present invention. In this embodiment, the method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics includes:

[0054] S1. Real-time acquisition of the original grayscale image of the target object, synchronous acquisition of the working environment temperature of the detector component, and preprocessing of the original grayscale image to obtain a preprocessed image.

[0055] This invention can acquire the original grayscale image of the target object in real time and retain the original radiation energy information of the complete temperature field without compression and pseudo-color mapping, providing a lossless data foundation for subsequent high-precision temperature inversion and extraction of subtle thermal fault features.

[0056] See Figure 2This is a schematic diagram of a continuous zoom infrared thermal imager based on a statistical characteristic infrared image vertical stripe dynamic suppression method according to an embodiment of the present invention. The continuous zoom infrared thermal imager comprises a zoom lens assembly, an infrared core assembly, and a power supply circuit. The zoom lens assembly includes a continuous zoom lens, a stepper motor, and a motor drive circuit, used to receive infrared radiation from the scene and focus it onto the focal plane of the infrared detector. Optical zoom and focusing operations can be performed by controlling the stepper motor. The infrared core assembly includes an infrared detector assembly, a preprocessing and interface circuit board, and an SDI circuit board. The infrared detector assembly includes an infrared detector and a cooler drive circuit. The detector is responsible for the photoelectric conversion of infrared radiation. The cooler drive circuit provides power and drive signals to the cooler, completing the closed-loop control of the cooler. The preprocessing and interface circuit board is responsible for conditioning, noise reduction filtering, non-uniformity correction, blind pixel replacement, digital detail enhancement (DDE), and stretching display of the signal output by the detector. The SDI circuit board transmits the image data to the back-end receiving and display device in SDI digital video format. It is also responsible for RS422 serial communication with the motor drive circuit and the power supply circuit. The power supply circuit converts the 28V power input from the external power supply equipment into the power signal used internally by the infrared thermal imager, with input and output ground isolation.

[0057] Specifically, the real-time acquisition of the original grayscale image of the target object includes:

[0058] The infrared radiation of the target object is focused onto the photosensitive surface of the infrared detector to obtain the initial infrared radiation energy;

[0059] Suppressing the thermal noise of the infrared detector itself in the initial infrared radiation energy yields suppressed infrared radiation energy;

[0060] The suppressed infrared radiation energy is converted into a raw electrical signal corresponding to the temperature distribution;

[0061] The original electrical signal is subjected to image conversion processing to generate digital image data;

[0062] The digital image data is transmitted to a display device, which then generates the original grayscale image.

[0063] Optionally, the process of focusing the infrared radiation of the target object onto the photosensitive surface of the infrared detector to obtain the initial infrared radiation energy can be achieved by driving the continuous zoom lens with a stepper motor in the zoom lens assembly.

[0064] Optionally, the suppression of the thermal noise of the infrared detector itself in the initial infrared radiation energy can be achieved by using a cooler drive circuit to drive the cooler inside the infrared detector, thereby reducing the temperature of the infrared detector to its operating temperature.

[0065] Optionally, the image conversion processing of the original electrical signal to generate digital image data can be achieved through preprocessing and interface circuitry.

[0066] Optionally, the transmission of the digital image data to the display device, and the generation of the original grayscale image by the display device, can be achieved through an RS422 communication interface.

[0067] The zoom lens assembly refers to an optical system used to adjust the field of view and focal length, consisting of a continuous zoom lens, a stepper motor, and a motor drive circuit. The stepper motor converts electrical pulse signals into precise angular displacement for accurate control of the focal length of the continuous zoom lens. The target object refers to an object whose surface temperature distribution is observed and measured by an infrared thermal imager. The infrared detector is a semiconductor device that converts received infrared radiation energy into electrical signals. The photosensitive surface is a microarray surface on the infrared detector that receives infrared radiation and generates a response. The initial infrared radiation energy refers to the unprocessed infrared energy emitted by the target object, containing both the target's own radiation and background radiation. The cooling machine drive circuit is an electronic circuit that provides drive current and control to the cooling machine inside the infrared detector. The cooling machine is a miniature cooling device integrated inside the infrared detector to reduce its temperature to extremely low levels. The operating temperature is... The term "infrared detector" refers to the low-temperature state that the infrared detector needs to maintain to achieve the target performance. "Thermal noise" refers to interference signals generated by the infrared detector's own temperature that are unrelated to the target radiation. "Suppressed infrared radiation energy" refers to the infrared radiation energy that reflects only the true temperature distribution of the target object after eliminating the influence of the detector's own thermal noise. "Original electrical signal" refers to the weak analog electrical signal output by the infrared detector. "Preprocessing and interface circuit" refers to the circuit module that outputs digital image data. "Digital image data" refers to pixel matrix data corresponding to the temperature distribution, represented in digital form. "RS422 communication interface" is used to reliably transmit digital image data to the display device. "Display device" refers to the device that receives data transmitted through the RS422 interface and is responsible for rendering the digital image data into a visual image. "Original grayscale image" refers to an image generated by the display device based on the digital image data, where the pixel grayscale values ​​directly correspond to the temperature levels without pseudo-color mapping.

[0068] Optionally, the step of receiving the original electrical signal through the preprocessing and interface circuit and processing the original electrical signal to generate digital image data includes amplification, filtering, and analog-to-digital conversion.

[0069] It should be explained that the detector assembly refers to the core part of an infrared thermal imager responsible for converting infrared radiation energy into electrical signals. It typically includes an infrared detector, a cooler, and its drive circuit. The operating environment temperature refers to the external ambient temperature of the infrared detector assembly, which is collected by a temperature sensor installed on the outer shell of the detector assembly.

[0070] The present invention preprocesses the original grayscale image to obtain a preprocessed image that can effectively suppress noise, correct distortion and enhance image contrast, thereby significantly improving the accuracy and reliability of subsequent temperature measurements.

[0071] Specifically, the preprocessing of the original grayscale image to obtain a preprocessed image includes:

[0072] The original grayscale image is subjected to defect removal processing to obtain a first processed grayscale image;

[0073] The first processed grayscale image is subjected to median filtering to obtain the preprocessed image.

[0074] The first processed grayscale image refers to an intermediate image that has eliminated interference from blind pixels and invalid pixels. The bad pixel removal process refers to the process of locating pixels whose grayscale values ​​in the original grayscale image exceed the preset effective dynamic range and replacing and repairing them with the average grayscale value of the effective pixels in the neighborhood of the pixel. The median filtering process refers to the process of selecting a filter window of a preset size centered on the current pixel, sorting the grayscale values ​​of all pixels in the window, and taking the median as the output grayscale value of the current pixel. The preprocessed image refers to image data that can characterize the effective scene thermal radiation features after bad pixel repair and noise suppression.

[0075] S2. Establish temperature segmentation intervals for the preprocessed image, and adaptively configure the correction parameters of the preprocessed image based on the matching results of the working environment temperature and the temperature segmentation intervals.

[0076] This invention establishes temperature segmentation intervals for the preprocessed image and adaptively matches the optimal correction parameters based on the detector's response characteristics under different ambient temperatures, thereby solving the problem of correction parameter mismatch caused by ambient temperature changes and significantly improving stripe suppression accuracy and image quality over a wide temperature range.

[0077] In detail, the temperature segmentation intervals include at least a low temperature segment, a normal temperature segment, and a high temperature segment, wherein the low temperature segment is T < 10℃, the normal temperature segment is 10℃ ≤ T ≤ 50℃, and the high temperature segment is T > 50℃; different temperature segmentation intervals correspond to different row statistical window sizes, column smoothing factors, and correction coefficient update step sizes.

[0078] The low-temperature range refers to the temperature range where the ambient temperature is below the lower limit of the detector's linear response region, causing drastic changes in response characteristics. The normal temperature range refers to the temperature range where the ambient temperature is within the detector's standard operating range, resulting in relatively stable response characteristics. The high-temperature range refers to the temperature range where the ambient temperature is above the upper limit of the detector's linear response region, leading to a significant increase in background noise. The row statistics window size refers to the number of rows selected in the vertical direction of the image for calculating column statistics features, used to balance the stability of statistical features with the fidelity of local details. The column smoothing factor is a parameter used to control the retention of historical frame coefficient weights in the recursive update of the dynamic correction coefficients in the time domain, used to suppress inter-frame correction jumps. The correction coefficient update step size is a parameter used to control the speed at which the current frame's fringe deviation coefficient is incorporated into the dynamic correction coefficients, used to adjust the correction response sensitivity.

[0079] This invention adaptively configures the correction parameters of the preprocessed image based on the matching results of the operating environment temperature and the temperature segmentation intervals. This allows for precise matching of the correction parameters required by the detector's current response characteristics according to changes in ambient temperature, effectively solving the problem of correction parameter mismatch caused by temperature drift and ensuring the real-time performance and accuracy of stripe suppression across the entire temperature range. The correction parameters refer to the row statistical window size, column smoothing factor, and correction coefficient update step size obtained from the temperature segmentation interval matching.

[0080] S3. Calculate the statistical feature value of each column of the preprocessed image after removing outliers, so as to calculate the global statistical mean of the preprocessed image, and calculate the column stripe deviation coefficient of the preprocessed image based on the global statistical mean and the statistical feature value.

[0081] The present invention calculates the statistical feature value of each column of the preprocessed image after removing outliers, which effectively eliminates the pulling effect of strong targets and complex backgrounds on the column statistical features, and significantly improves the representation accuracy of the statistical feature value on the response of the real background.

[0082] Specifically, calculating the statistical feature value of each column of the preprocessed image after removing outliers includes:

[0083] The gray values ​​of each column of pixels in the preprocessed image are sorted to obtain a pixel sequence. High gray value pixels and low gray value pixels with a preset percentage threshold are removed from the pixel sequence to obtain the target pixel sequence.

[0084] Based on the row statistical window size of the correction parameters corresponding to the preprocessed image, the range of the statistical window in the row direction is determined in the target pixel sequence, and the gray mean within the statistical window range is calculated as the statistical feature value of the current column of the preprocessed image after removing outliers.

[0085] Wherein, the pixel sequence refers to an ordered set composed of all pixels in the current column arranged in ascending order of grayscale values; the preset percentage threshold refers to a set parameter representing the proportion of high-grayscale pixels to low-grayscale pixels in the total number of pixels in the current column, used to define the number of outliers to be removed; the preset percentage threshold in this invention can be the maximum 5% and minimum 5% grayscale values ​​within the removal window; the target pixel sequence refers to the set of effective pixels reflecting background radiation characteristics retained after removing extreme value interference; the grayscale mean refers to the arithmetic mean of the grayscale values ​​of all pixels in the target pixel sequence; and the statistical feature value refers to a value used to characterize the background response benchmark of the current column, serving as the basis for calculating the stripe deviation coefficient.

[0086] It should be explained that the global statistical mean refers to the average value reflecting the overall background radiation intensity of the preprocessed image, which serves as a unified reference benchmark for evaluating the response differences of each column.

[0087] This invention calculates the column stripe deviation coefficient of the preprocessed image based on the global statistical mean and the statistical feature value, compares the deviation between the column statistical feature value and the global statistical mean, realizes the quantitative characterization of column-level response differences, provides an accurate correction basis for non-uniformity correction, and effectively eliminates column stripe noise.

[0088] Specifically, calculating the column stripe deviation coefficient of the preprocessed image based on the global statistical mean and the statistical feature values ​​includes:

[0089] The difference between the statistical feature value of each column of the preprocessed image and the global statistical mean is calculated to obtain the column response deviation;

[0090] Based on the step size of the correction coefficients of the correction parameters corresponding to the preprocessed image, a low-pass filter for the preprocessed image is constructed.

[0091] The column response deviation is input to the low-pass filter for smoothing to obtain the column stripe deviation coefficient.

[0092] The column response deviation refers to the deviation of the background response gray value of the current column from the overall background reference of the image, which is used to characterize the degree of non-uniformity of the pixels in that column. The column stripe deviation coefficient refers to the offset parameter used to correct column stripe noise after smoothing, which reflects the fixed response difference between columns. The low-pass filter refers to a filtering algorithm or circuit module used to filter out the instantaneous fluctuations caused by random noise or high-frequency scene details in the column response deviation and retain stable column stripe feature components.

[0093] S4. Use the correction parameters to perform time-domain recursive smoothing on the column stripe deviation coefficient to generate dynamic correction coefficients.

[0094] This invention utilizes the correction parameters to perform time-domain recursive smoothing on the column stripe deviation coefficients, generating dynamic correction coefficients that effectively suppress the impact of random noise and instantaneous scene interference on the correction parameters, ensuring the continuity and stability of the dynamic correction coefficients in the time dimension, and significantly reducing the flickering and jittering of the corrected image.

[0095] In detail, the step of using the correction parameters to perform time-domain recursive smoothing on the column stripe deviation coefficient to generate dynamic correction coefficients includes:

[0096] The temporal smoothing factor of the preprocessed image is determined based on the correction parameters;

[0097] For the column stripe deviation coefficient of the current frame of the preprocessed image:

[0098] If the current frame is the starting frame, then the column stripe deviation coefficient is used as the dynamic correction coefficient of the current frame of the preprocessed image;

[0099] If the current frame is not the starting frame, the dynamic correction coefficient of the previous frame of the preprocessed image and the column stripe deviation coefficient of the current frame are weighted and summed using the temporal smoothing factor to generate the dynamic correction coefficient of the current frame of the preprocessed image.

[0100] The temporal smoothing factor refers to the weight used to control the smoothing of the dynamic correction coefficient of the preprocessed image in the time dimension, and the dynamic correction coefficient refers to the column correction coefficient that changes over time to correct column stripes in the preprocessed image.

[0101] Further, the step of using the temporal smoothing factor to perform a weighted summation of the dynamic correction coefficients of the previous frame of the preprocessed image and the column stripe deviation coefficients of the current frame to generate the dynamic correction coefficients of the current frame of the preprocessed image includes:

[0102] Using the temporal smoothing factor, the dynamic correction coefficients of the previous frame and the column stripe deviation coefficients of the current frame of the preprocessed image are weighted and summed using the following formula to generate the dynamic correction coefficients of the current frame of the preprocessed image:

[0103] ;

[0104] in, This represents the dynamic correction coefficients of the current frame of the preprocessed image. Represents the time-domain smoothing factor. This represents the dynamic correction coefficient of the previous frame of the preprocessed image. This indicates the stripe deviation coefficient.

[0105] S5. Using the dynamic correction coefficient and a preset gradient threshold, the preprocessed image is corrected column by column to obtain the target image after vertical stripe suppression of the preprocessed image.

[0106] This invention uses the dynamic correction coefficient, combined with a preset gradient threshold, to perform column-by-column correction on the preprocessed image, resulting in a target image after vertical stripe suppression. By introducing the joint constraint of the dynamic correction coefficient and the gradient threshold, this method can effectively remove column stripe noise while protecting image details based on image edge gradient information, avoiding edge blurring or artifacts caused by overcorrection in existing technologies, and significantly improving the visual quality of the image.

[0107] Specifically, the step of correcting the preprocessed image column by column using the dynamic correction coefficients and a preset gradient threshold to obtain the target image after suppressing the vertical stripes of the preprocessed image includes:

[0108] Calculate the gradient magnitude of each column of pixels in the preprocessed image in the row direction;

[0109] The gradient magnitude is compared with the gradient threshold to generate a control signal for controlling the correction intensity of the preprocessed image;

[0110] By combining the control signal and the dynamic correction coefficient, the preprocessed image is corrected column by column to output the target image after vertical stripe suppression.

[0111] Wherein, the gradient magnitude refers to the absolute value of the difference in grayscale values ​​between adjacent pixels in the row direction of each column of pixels in the preprocessed image; the gradient threshold refers to a preset critical value for the grayscale change amplitude used to distinguish between texture edge regions and flat regions; the control signal refers to an instruction generated based on the comparison result of the gradient magnitude and the gradient threshold, used to indicate whether to perform full correction, attenuation correction, or skip correction on the current column of pixels; and the target image refers to an image whose column stripe noise is suppressed and image edge details are preserved after the column-by-column correction.

[0112] Further, comparing the gradient magnitude with the gradient threshold to generate a control signal for controlling the preprocessed image correction intensity includes:

[0113] If the gradient magnitude is greater than or equal to the gradient threshold, the texture edge region of the preprocessed image is determined to generate a first control signal for suppression correction.

[0114] If the gradient magnitude is less than the gradient threshold, a flat region of the preprocessed image is determined to generate a second control signal for performing full correction.

[0115] Based on the first control signal and the second control signal, a control signal for the preprocessed image correction intensity is generated.

[0116] The textured edge region refers to the region in the preprocessed image where the pixel grayscale value changes drastically and contains image detail features. The first control signal is a control instruction used to indicate reducing the correction intensity to preserve image edge details. The flat region refers to the region in the preprocessed image where the pixel grayscale value changes gently and has no obvious edge features. The second control signal is a control instruction used to indicate performing full correction to eliminate column stripe noise.

[0117] Further, the step of combining the control signal and the dynamic correction coefficient to perform column-by-column correction on the preprocessed image to output the target image after vertical stripe suppression of the preprocessed image includes:

[0118] During the traversal of each column of the preprocessed image, the control signal type for each column in the preprocessed image is determined:

[0119] When the control signal type belongs to the first control signal, the current column pixels are smoothed according to the dynamic correction coefficient to obtain smoothed pixels, and the smoothed pixels are used as the first column pixels of the output image.

[0120] When the control signal type is the second control signal, the dynamic correction coefficient is used to perform column stripe correction on the current column pixels to obtain the corrected pixels, and the corrected pixels are used as the second column pixels of the output image;

[0121] After traversing each column of the preprocessed image, a target image with vertical stripe suppression of the output image is generated based on the pixels of the first column and the pixels of the second column.

[0122] The smoothing process refers to applying an attenuation correction coefficient to the current column pixel to reduce the correction intensity. The smoothed pixel refers to the pixel value that retains edge features after the smoothing process. The column stripe correction refers to subtracting the dynamic correction coefficient from the gray value of the current column pixel to eliminate column stripe noise. The corrected pixel refers to the pixel value that eliminates column stripe noise after the column stripe correction. The target image refers to the final image composed of the corresponding smoothed pixels and the corresponding corrected pixels in columns, and where vertical stripe noise is suppressed.

[0123] Furthermore, the gradient threshold is determined based on the product of the grayscale dynamic range span of the preprocessed image and a preset scaling factor.

[0124] Wherein, the gray-scale dynamic range span refers to the difference between the maximum and minimum gray-scale values ​​of all pixels in the preprocessed image, and the preset proportional coefficient is used to characterize the proportion weight of the gradient threshold in the gray-scale dynamic range of the preprocessed image. By adjusting the size of the preset proportional coefficient, the sensitivity to noise filtering in flat areas and texture preservation in edge areas of the image is controlled. In detail, the preset proportional coefficient is pre-calibrated based on the response characteristics of the infrared detector and the statistical characteristics of stripe noise.

[0125] In one embodiment, a computer device is provided, which may be a server or a client, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external clients via a network connection. When executed by the processor, the computer program implements the functions or steps of the server or client side of the statistical characteristic infrared image vertical stripe dynamic suppression method.

[0126] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0127] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0128] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0129] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0130] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics, characterized in that, The method includes: The original grayscale image of the target object is acquired in real time, and the operating temperature of the detector component is acquired simultaneously. The original grayscale image is preprocessed to obtain a preprocessed image. Establish temperature segmentation intervals for the preprocessed image, and adaptively configure the correction parameters of the preprocessed image based on the matching results between the working environment temperature and the temperature segmentation intervals. Calculate the statistical feature value of each column of the preprocessed image after removing outliers, so as to calculate the global statistical mean of the preprocessed image, and calculate the column stripe deviation coefficient of the preprocessed image based on the global statistical mean and the statistical feature value. The column stripe deviation coefficient is recursively smoothed in the temporal domain using the correction parameters to generate dynamic correction coefficients. This process includes: determining a temporal smoothing factor for the preprocessed image based on the correction parameters; for the column stripe deviation coefficient of the current frame of the preprocessed image, if the current frame is the starting frame, then using the column stripe deviation coefficient as the dynamic correction coefficient of the current frame of the preprocessed image; if the current frame is not the starting frame, then using the temporal smoothing factor, a weighted sum of the dynamic correction coefficient of the previous frame of the preprocessed image and the column stripe deviation coefficient of the current frame is performed to generate the dynamic correction coefficient of the current frame of the preprocessed image. By using the dynamic correction coefficients and a preset gradient threshold, the preprocessed image is corrected column by column to obtain the target image after vertical stripe suppression.

2. The method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics as described in claim 1, characterized in that, By using the dynamic correction coefficients and a preset gradient threshold, the preprocessed image is corrected column by column to obtain the target image after vertical stripe suppression, including: Calculate the gradient magnitude of each column of pixels in the preprocessed image in the row direction; The gradient magnitude is compared with the gradient threshold to generate a control signal for controlling the correction intensity of the preprocessed image; By combining the control signal and the dynamic correction coefficient, the preprocessed image is corrected column by column to output the target image after vertical stripe suppression.

3. The method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics as described in claim 2, characterized in that, The gradient magnitude is compared with the gradient threshold to generate a control signal for controlling the correction intensity of the preprocessed image, including: If the gradient magnitude is greater than or equal to the gradient threshold, the texture edge region of the preprocessed image is determined to generate a first control signal for suppression correction. If the gradient magnitude is less than the gradient threshold, a flat region of the preprocessed image is determined to generate a second control signal for performing full correction. Based on the first control signal and the second control signal, a control signal for the preprocessed image correction intensity is generated.

4. The method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics as described in claim 2, characterized in that, Combining the control signal and the dynamic correction coefficient, the preprocessed image is corrected column by column to output the target image after vertical stripe suppression, including: During the traversal of each column of the preprocessed image, the control signal type for each column in the preprocessed image is determined: When the control signal type belongs to the first control signal, the current column pixels are smoothed according to the dynamic correction coefficient to obtain smoothed pixels, and the smoothed pixels are used as the first column pixels of the output image. When the control signal type is the second control signal, the dynamic correction coefficient is used to perform column stripe correction on the current column pixels to obtain the corrected pixels, and the corrected pixels are used as the second column pixels of the output image; After traversing each column of the preprocessed image, a target image with vertical stripe suppression of the output image is generated based on the pixels of the first column and the pixels of the second column.

5. The method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics as described in claim 2, characterized in that, The gradient threshold is determined based on the product of the grayscale dynamic range span of the preprocessed image and a preset scaling factor.

6. The method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics as described in claim 1, characterized in that, Real-time acquisition of the original grayscale image of the target object, including: The infrared radiation of the target object is focused onto the photosensitive surface of the infrared detector to obtain the initial infrared radiation energy; Suppressing the thermal noise of the infrared detector itself in the initial infrared radiation energy yields suppressed infrared radiation energy; The suppressed infrared radiation energy is converted into a raw electrical signal corresponding to the temperature distribution; The original electrical signal is subjected to image conversion processing to generate digital image data; The digital image data is transmitted to a display device, which then generates the original grayscale image.

7. The method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics as described in claim 1, characterized in that, The original grayscale image is preprocessed to obtain a preprocessed image, including: The original grayscale image is subjected to defect removal processing to obtain a first processed grayscale image; The first processed grayscale image is subjected to median filtering to obtain the preprocessed image.

8. The method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics as described in claim 1, characterized in that, Calculate the statistical feature value of each column of the preprocessed image after removing outliers, including: The gray values ​​of each column of pixels in the preprocessed image are sorted to obtain a pixel sequence. High gray value pixels and low gray value pixels with a preset percentage threshold are removed from the pixel sequence to obtain the target pixel sequence. Based on the row statistical window size of the correction parameters corresponding to the preprocessed image, the range of the statistical window in the row direction is determined in the target pixel sequence, and the gray mean within the statistical window range is calculated as the statistical feature value of the current column of the preprocessed image after removing outliers.

9. The method for dynamic suppression of vertical stripes in infrared images based on statistical characteristics as described in claim 1, characterized in that, Based on the global statistical mean and the statistical characteristic values, the column stripe deviation coefficient of the preprocessed image is calculated, including: The difference between the statistical feature value of each column of the preprocessed image and the global statistical mean is calculated to obtain the column response deviation; Based on the step size of the correction coefficients of the correction parameters corresponding to the preprocessed image, a low-pass filter for the preprocessed image is constructed. The column response deviation is input to the low-pass filter for smoothing to obtain the column stripe deviation coefficient.

Images