A rock thin section self-adaptive imaging optimization method and system suitable for field environment

Abstract

The application discloses a kind of rock slice self-adaptive imaging optimization method and system suitable for field environment, it is related to image processing technical field.Method includes: obtaining portable rock slice field rapid analysis workstation;Start portable rock slice field rapid analysis workstation to collect field environment data and execute parameter calibration, obtain initial imaging calibration parameter;Rock slice is fixed in adjustable clamping mechanism, selected target optical mode and switch, based on target optical mode and initial imaging calibration parameter drive camera to rock slice and carry out adaptive focusing imaging, obtain multiple mineral images;Multiple mineral images are carried out multiple superposition denoising and edge enhancement, obtain high-definition mineral image, and carry out imaging quality evaluation and compensation optimization.Solve the technical problem that rock slice imaging quality is unstable under field environment in prior art, mineral detail recognition effect is poor, reach the technical effect of improving rock slice imaging definition and stability.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and specifically to an adaptive imaging optimization method and system for rock thin sections suitable for field environments. Background Technology

[0002] Rock thin section analysis is a crucial technique in geological surveys, mineral identification, petrogenesis studies, and mineral resource exploration. Through microscopic observation and image analysis of rock thin sections, mineral types, structural features, and inter-mineral relationships can be identified, providing vital evidence for geological research and resource evaluation. Currently, rock thin section analysis typically relies on laboratory instruments such as polarizing microscopes and digital microscopy imaging equipment for image acquisition and analysis. However, most existing rock thin section imaging equipment is bulky and dependent on stable laboratory environments and fixed power supplies, making rapid deployment and real-time analysis difficult during field geological surveys. Furthermore, the field environment presents challenges such as significant variations in lighting, potential equipment misalignment, and substantial temperature and humidity fluctuations, which can lead to increased noise, inaccurate focusing, blurred details, and unstable image quality in the acquired rock thin section images, affecting the accuracy of mineral feature identification and on-site analysis results. Summary of the Invention

[0003] This application provides an adaptive imaging optimization method and system for rock thin sections suitable for field environments, which solves the technical problems of unstable imaging quality and poor mineral detail recognition in existing technologies for rock thin sections in field environments.

[0004] The first aspect of this application provides an adaptive imaging optimization method for rock thin sections suitable for field environments, the method comprising: A portable rock thin section rapid analysis workstation was acquired, comprising a lightweight body, a multi-optical mode integrated module, a high-definition imaging and sensing unit, an intelligent interactive terminal, an adjustable clamping mechanism, and a power supply unit. The workstation was activated to collect field environmental data and perform parameter calibration to obtain initial imaging calibration parameters. The rock thin section was fixed to the adjustable clamping mechanism, a target optical mode was selected and switched, and the camera was driven to perform adaptive focusing imaging of the rock thin section based on the target optical mode and the initial imaging calibration parameters, acquiring multiple frames of mineral images. These multiple frames of mineral images were then subjected to multi-frame stacking noise reduction and edge enhancement to obtain high-definition mineral images, and imaging quality assessment and compensation optimization were performed based on these high-definition mineral images.

[0005] A second aspect of this application provides an adaptive imaging optimization system for rock thin sections suitable for field environments, the system comprising: Machine Interaction Component: Acquires a portable rock thin section rapid analysis workstation, which includes a lightweight body, a multi-optical mode integrated module, a high-definition imaging and sensing unit, an intelligent interactive terminal, an adjustable clamping mechanism, and a power supply unit; Parameter Calibration Component: Starts the portable rock thin section rapid analysis workstation to collect field environmental data and perform parameter calibration to obtain initial imaging calibration parameters; Adaptive Focusing Imaging Component: Fixes the rock thin section to the adjustable clamping mechanism, selects and switches the target optical mode, and drives the camera to perform adaptive focusing imaging of the rock thin section based on the target optical mode and the initial imaging calibration parameters to acquire multiple frames of mineral images; Image Processing Component: Performs multi-frame superposition noise reduction and edge enhancement on the multiple frames of mineral images to obtain high-definition mineral images, and performs imaging quality assessment and compensation optimization based on the high-definition mineral images.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, a portable rock thin section rapid analysis workstation was acquired. This workstation includes a lightweight body, a multi-optical mode integrated module, a high-definition imaging and sensing unit, an intelligent interactive terminal, an adjustable clamping mechanism, and a power supply unit. Next, the workstation was activated to collect field environmental data and perform parameter calibration to obtain initial imaging calibration parameters. Then, the rock thin section was fixed to the adjustable clamping mechanism, a target optical mode was selected and switched, and the camera was driven to adaptively focus and image the rock thin section based on the target optical mode and the initial imaging calibration parameters, acquiring multiple frames of mineral images. Finally, multi-frame superposition noise reduction and edge enhancement were performed on the multiple mineral images to obtain high-definition mineral images, and imaging quality assessment and compensation optimization were performed based on these high-definition mineral images. This method solves the technical problems of unstable imaging quality and poor mineral detail recognition in existing technologies for rock thin sections under field conditions, achieving the technical effect of improving the clarity and stability of rock thin section imaging. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A schematic diagram of an adaptive imaging optimization method for rock thin sections applicable to field environments is provided in an embodiment of this application. Figure 2 This is a schematic diagram of a rock thin section adaptive imaging optimization system suitable for field environments, provided as an embodiment of this application.

[0009] Figure labeling: Machine interaction component 11, parameter calibration component 12, adaptive focusing imaging component 13, image processing component 14. Detailed Implementation

[0010] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0011] Example 1, as Figure 1 As shown, this application provides an adaptive imaging optimization method for rock thin sections suitable for field environments, wherein the method includes: A portable rock thin section rapid analysis workstation was obtained, which includes a lightweight body, a multi-optical mode integrated module, a high-definition imaging and sensing unit, an intelligent interactive terminal, an adjustable clamping mechanism, and a power supply unit.

[0012] In this embodiment, the portable rock thin section rapid on-site analysis workstation serves as the basic execution carrier for on-site imaging and acquisition of rock thin sections. Its lightweight body is integrally molded from carbon fiber composite material, with dimensions of 28cm×18cm×12cm and a total weight controlled within 2.8kg, to meet the needs of geologists for backpack carrying and field operations. The lightweight body is wrapped with an EVA shock-absorbing buffer layer to reduce the impact of vibration and shock generated during transportation, handheld operation, or ground placement on imaging stability. The body is equipped with a heat insulation layer inside, enabling the workstation to withstand continuous operation under strong outdoor light, temperature and humidity changes, and environmental conditions ranging from -10℃ to 50℃. A detachable strong light shield is installed on the top of the body. In use, the strong light shield is attached above the imaging window to block direct outdoor light and side stray light, reducing the interference of strong external light on the microscopic imaging of rock thin sections. The multi-optical mode integrated module is installed in the imaging optical path of the lightweight body. The module is equipped with a polarizer, analyzer, conodolite, and differential interferometer. The corresponding optical lenses are driven into or out of the imaging optical path by an electric rotating slot switching mechanism. After receiving the optical mode switching command output by the intelligent interactive terminal, the electric rotating slot switching mechanism drives the target slot to rotate to the preset positioning position, so that the corresponding optical lens is quickly switched into place. The switching response time is controlled within 0.3 seconds and the positioning accuracy is controlled within 0.01mm. This enables one-click switching of four rock and mineral identification optical modes: single polarization, orthogonal polarization, conodolite, and differential interferometer. This avoids the problems of contamination, misalignment, and low operation efficiency caused by manually disassembling and assembling optical components in the field. The high-definition imaging and sensing unit is located at the end of the imaging optical path and includes a 20-megapixel low-light CMOS microscopy camera, an adjustable-focus LED cold light source, a miniature tilt sensor, a temperature and humidity sensor, and a light sensor. The CMOS microscopy camera is used to acquire microscopic mineral images of thin rock sections. The adjustable-focus LED cold light source provides stable illumination to the thin rock sections, with brightness infinitely adjustable from 0 to 2000 lux to adapt to imaging needs in low light, strong light obstruction, and thin sections with different transmittances, while avoiding the thermal impact of high-temperature illumination on the thin rock sections. The miniature tilt sensor is used to acquire the workstation's placement orientation, the temperature and humidity sensor is used to acquire on-site temperature and humidity parameters, and the light sensor is used to acquire ambient light intensity. The acquired field environmental parameters are transmitted to the intelligent interactive terminal as the data basis for subsequent imaging parameter calibration and adaptive compensation.The intelligent interactive terminal is installed on the outer or upper surface of the lightweight body. It features a built-in 2.8-inch high-definition touchscreen display, allowing users to select optical modes, view imaging parameters, initiate image acquisition, and browse results without needing an external computer. The intelligent interactive terminal also has a reserved Type-C interface for connecting to a mobile phone or tablet to expand the display and control interface. It also uses a built-in data storage unit to locally save acquired mineral images, environmental data, imaging parameters, and identification results. The adjustable clamping mechanism is located in the imaging area and employs a flexible clamping structure. The clamping distance can be adjusted according to the specifications of the rock thin sections to accommodate standard 25×75mm rock thin sections, optical sections, and smaller 10×10mm sections. During clamping, the elastic clamping force limits and fixes the rock thin section, ensuring its stability during field vibrations or slight changes in the workstation's posture, preventing field drift and image blurring caused by thin section displacement. The power supply unit is built into the lightweight body and includes an 8000mAh rechargeable lithium battery and a power management circuit. It is used to supply power to the multi-optical mode integrated module, the high-definition imaging and sensing unit, the intelligent interactive terminal and the adjustable clamping mechanism. The power supply unit supports continuous operation for more than 10 hours and supports fast charging mode to complete charging in 2 hours, so as to meet the needs of all-weather on-site imaging and analysis of rock thin sections under the condition of no stable power supply in the field.

[0013] The portable rock thin section field rapid analysis workstation was activated to collect field environmental data and perform parameter calibration to obtain initial imaging calibration parameters.

[0014] After activating the portable rock thin section rapid analysis workstation, the light sensor, temperature and humidity sensor, and miniature tilt sensor in the high-definition imaging and sensing unit first enter real-time monitoring mode and continuously collect field environmental data according to a preset sampling period. This field environmental data includes at least ambient light intensity, ambient temperature, ambient humidity, and the equipment tilt angle. The intelligent interactive terminal performs time synchronization and outlier removal on the collected field environmental data to form a current environmental state dataset. Subsequently, it calls pre-built environmental type judgment rules to classify and identify the current environmental state dataset. When the ambient light intensity is greater than a preset strong light threshold, it is determined to be a strong light environment; when the ambient light intensity is less than a preset weak light threshold, it is determined to be a weak light environment; and when the ambient humidity is greater than a preset high humidity threshold, it is determined to be a high humidity environment. When the device tilt angle exceeds a preset attitude offset threshold, it is determined to be a tilted environment, and the current environment type is generated based on the determination results. Then, the pre-stored environment-imaging parameter mapping model is read, and the current environment type is used as the input parameter for matching and querying to obtain the corresponding imaging parameter combination. The imaging parameter combination includes camera exposure time, image gain value, LED cold light source brightness, white balance parameters, and initial focus search range. Furthermore, the imaging parameter combination is corrected and calculated by combining the real-time collected ambient light intensity, temperature and humidity, and device attitude parameters to obtain the initial imaging calibration parameters adapted to the current field environment. The initial imaging calibration parameters are then loaded into the high-definition imaging and sensing unit to provide basic parameter configuration for subsequent optical mode switching, adaptive focus imaging, and mineral image acquisition.

[0015] Furthermore, the portable rock thin section rapid field analysis workstation is activated to collect field environmental data and perform parameter calibration to obtain initial imaging calibration parameters, including: An environment type determination rule is constructed, and the field environment data is used to determine the current environment type. Based on the field environment imaging measurement data, an environment-imaging parameter mapping model is preset. The environment-imaging parameter mapping model is called to calibrate the parameters of the current environment type to obtain the initial imaging calibration parameters.

[0016] Preferably, the current field environment data is simultaneously collected by the light sensor, temperature and humidity sensor, and miniature tilt sensor in the high-definition imaging and sensing unit. This field environment data includes ambient light intensity, ambient temperature, ambient humidity, and equipment tilt angle. Subsequently, an environment type judgment rule is constructed. Specifically, based on a large number of field environment imaging experiments, classification thresholds corresponding to each environmental parameter are pre-set. When the ambient light intensity is greater than 8000 lux, it is judged as a strong light environment; when the ambient light intensity is less than 500 lux, it is judged as a weak light environment; when the ambient humidity is greater than 85%, it is judged as a high humidity environment; and when the equipment tilt angle is greater than 3°, it is judged as an attitude deviation environment. The current environment type is determined based on the comprehensive judgment result of each parameter. Then, an environment-imaging parameter mapping model is pre-set based on the measured field environment imaging data. Specifically, rock thin section imaging samples are collected under different light intensities, temperature and humidity conditions, and equipment attitude conditions. The optimal imaging quality is statistically obtained. The system determines the corresponding exposure time, image gain, LED light source brightness, white balance parameters, and initial focus search range, and establishes a mapping relationship between environment type and imaging parameters. After determining the current environment type, it calls the environment-imaging parameter mapping model for parameter matching, reads the target imaging parameter combination corresponding to the current environment type, and performs correction and compensation based on the currently acquired environmental parameters. Specifically, when the ambient light intensity increases, the exposure time and image gain are reduced; when the ambient light intensity decreases, the exposure time and image gain are increased; when the ambient humidity increases, the LED light source brightness is increased to compensate for the influence of environmental scattering; and when the device is tilted, the initial focus search range is increased to improve the stability of subsequent autofocus. Finally, the system generates exposure time parameters, image gain parameters, LED light source brightness parameters, white balance parameters, and initial focus search range parameters that match the current field environment as initial imaging calibration parameters, which are then loaded into the high-definition imaging and sensing unit to complete parameter calibration.

[0017] The rock thin section is fixed to the adjustable clamping mechanism, the target optical mode is selected and switched, and the camera is driven to perform adaptive focusing imaging of the rock thin section based on the target optical mode and the initial imaging calibration parameters to acquire multiple frames of mineral images.

[0018] Specifically, after fixing the rock thin section to the adjustable clamping mechanism, the rock thin section is limited and fixed by the elastic clamping structure, so that the rock thin section is in a stable imaging position, avoiding sample displacement caused by vibration of the field environment or changes in equipment posture; then, the target optical mode corresponding to the current rock observation needs is selected through the intelligent interactive terminal, and a mode switching command is sent to the multi-optical mode integration module. The electric rotating slot switching mechanism drives the corresponding optical lens into the imaging optical path to complete the rapid switching between single polarization mode, orthogonal polarization mode, conical mode, or differential interference mode; after the target optical mode switching is completed, the previously obtained initial imaging calibration parameters are read, and the target optical mode is then used to determine the appropriate mode. The optical characteristics corresponding to the mode are adapted and adjusted for exposure time, image gain, light source brightness, white balance parameters, and focus search range to generate target imaging parameters corresponding to the current imaging task. Then, according to the target imaging parameters, the high-definition imaging and sensing unit is activated to automatically scan and preview the rock thin section, establish an overall field-of-view image of the rock thin section, and perform target region search and adaptive focus control based on the overall field-of-view image. After the focus optimization is completed, the microscope camera is controlled to continuously acquire multiple frames of mineral images in the target imaging area, while recording the imaging parameters and acquisition status information corresponding to each frame of image, thereby obtaining a multi-frame mineral image dataset for subsequent image fusion and enhancement processing.

[0019] Furthermore, based on the target optical mode and the initial imaging calibration parameters, the camera is driven to perform adaptive focusing imaging of the rock thin section to acquire multiple frames of mineral images, including: The initial imaging calibration parameters are adjusted and compensated twice based on the target optical mode to obtain the target imaging calibration parameters; the camera is driven to automatically scan the rock thin section according to the target imaging calibration parameters to obtain a global rock preview image; mineral region identification and adaptive focusing imaging are performed on the global rock preview image to obtain multiple frames of mineral images.

[0020] First, the initial imaging calibration parameters are adjusted for secondary compensation based on the currently selected target optical mode. Specifically, this involves reading the preset compensation parameter library corresponding to different optical modes. When the target optical mode is a single-polarization mode, the initial imaging calibration parameters remain unchanged. When the target optical mode is a cross-polarization mode, the exposure time and image gain are appropriately increased to compensate for the brightness loss caused by the decrease in polarized light transmittance. When the target optical mode is a conical mode, the LED light source brightness is increased and the focus search range is expanded to meet the needs of interferometric pattern observation. When the target optical mode is a differential interferometry mode, the white balance parameters and image contrast parameters are adjusted to enhance the mineral boundary display effect, thereby obtaining target imaging calibration parameters that match the current target optical mode. Subsequently, the high-resolution imaging is driven according to the target imaging calibration parameters. The imaging and sensing unit performs automatic scanning, acquiring rock thin section images region by region by controlling the platform or imaging field of view according to a preset scanning path. The images of each region are then stitched together and reconstructed to generate a global rock preview image covering the entire observation area of ​​the rock thin section. Subsequently, the global rock preview image is subjected to image analysis processing. By identifying color differences, texture features, edge distribution, and mineral grain aggregation characteristics, mineral regions with analytical value are determined. An adaptive focusing imaging process is initiated within the identified mineral regions, automatically adjusting the focal plane position according to changes in image sharpness in the mineral regions to optimize the focus of the target region. After reaching the optimal imaging state, multiple frames of mineral images are continuously acquired to obtain a multi-frame mineral image dataset for subsequent image fusion and enhancement processing.

[0021] For example, in a low-light environment, the initial imaging calibration parameters are set as follows: exposure time 40ms, image gain 2.5x, LED light source brightness 1500 lux, white balance color temperature 5000K, and initial focus search range ±1.5mm. When the user selects the cross-polarization mode as the target optical mode, the system calls the compensation coefficient corresponding to the cross-polarization mode to adjust the exposure time to 60ms, the image gain to 3.0x, the LED light source brightness to 1950 lux, the white balance color temperature to 5000K, and the initial focus search range to ±1.5mm, thus obtaining the target imaging calibration parameters. Subsequently, the camera performs automatic rock thin section scanning with an exposure time of 60ms, an image gain of 3.0x, an LED light source brightness of 1950 lux, a white balance color temperature of 5000K, and an initial focus search range of ±1.5mm to obtain a full-area rock preview image. Adaptive focusing and multi-frame mineral image acquisition are then performed within the identified mineral areas.

[0022] Furthermore, mineral region identification and adaptive focusing imaging are performed on the global rock preview image to obtain multiple frames of mineral images, including: Mineral regions are identified and their locations are marked on the global rock preview image to obtain a list of target mineral region coordinates; adaptive coarse focusing is performed based on the target mineral region coordinate list to determine the initial focus of the target region; fine focusing and multi-frame image acquisition are performed based on the initial focus of the target region to obtain multiple frames of mineral images.

[0023] First, the global rock preview image is preprocessed by grayscale normalization, contrast enhancement, and noise suppression to improve the distinguishability between mineral boundaries and background areas. Then, based on color distribution features, texture features, edge features, and mineral grain morphology features, region analysis is performed on the global rock preview image to identify mineral aggregation areas, mineral boundary areas, and representative mineral structure areas in the rock thin section. The positional coordinates of each mineral region in the global rock preview image are determined, generating a target mineral region coordinate list. Finally, based on the target mineral region coordinate list, the imaging field of view is moved to the corresponding mineral region, and the process is initiated. The dynamic adaptive coarse focusing process quickly determines the clear imaging range of the target mineral region through a large-scale focal plane search, thereby obtaining the initial focus of the target region. After obtaining the initial focus of the target region, fine focusing is further performed with the initial focus of the target region as the center, and the focal plane is finely adjusted within a small range to obtain the optimal imaging focus of the target mineral region. When the optimal imaging focus is determined, the high-definition imaging and sensing unit is controlled to continuously acquire multiple frames of mineral images at the optimal imaging focus position, and simultaneously record the corresponding focus position parameters and imaging parameter information, thereby forming a multi-frame mineral image dataset for subsequent image fusion and enhancement processing.

[0024] Furthermore, adaptive coarse focusing is performed based on the target mineral region coordinate list to determine the initial focus of the target region, including: Based on the target mineral region coordinate list, the focusing motor is driven to continuously acquire images according to a preset coarse adjustment step size to obtain a multi-frame region image set; an image evaluation function is selected, and the multi-frame region image set is evaluated according to the image evaluation function to determine a multi-frame image evaluation value set; based on the multi-frame image evaluation value set, adaptive coarse adjustment focusing is performed on the multi-frame region image set to determine the initial focus of the target region.

[0025] Preferably, the platform or imaging field of view is moved to the corresponding mineral region according to the target mineral region coordinate list, and the focusing motor is driven to perform focal plane scanning at a preset coarse adjustment step size, wherein the preset coarse adjustment step size is preferably 20μm to 50μm; during the scanning process, a frame of regional image is acquired for each coarse adjustment step size, thereby obtaining a set of multiple frames of regional images covering different focal planes of the target mineral region; then, the Laplacian variance is selected as the image evaluation function to evaluate the sharpness of each frame of regional image, specifically by first performing grayscale processing on the regional image, and then using the Laplacian operator to extract the sharpness of the image. The edge change information is obtained, and the variance of the Laplacian response is calculated. The obtained variance is used as the evaluation value of the corresponding region image, thus forming a multi-frame image evaluation value set that corresponds one-to-one with the multi-frame region images. Then, the multi-frame image evaluation value set is compared and analyzed according to the focal plane position order to determine the focal plane position corresponding to the region image with the largest evaluation value, and this focal plane position is used as the optimal coarse adjustment focus position. At the same time, with the optimal coarse adjustment focus position as the center, a preset focal plane range is extended forward and backward as the search interval for subsequent fine adjustment focus, thereby determining the initial focus of the target region.

[0026] Furthermore, based on the initial focus of the target area, fine-tuning focus processing and multi-frame image acquisition are performed to obtain multiple frames of mineral images, including: Based on the initial focus of the target area, the fine-tuning position is predicted to obtain the fine-tuning starting position; with the fine-tuning starting position as the acquisition center, multiple frames of images are acquired according to the preset fine-tuning step size and evaluated and filtered by the image evaluation function to determine the fine-tuning focus of the target area; based on the fine-tuning focus of the target area, focus locking compensation and multiple frame image acquisition are performed to obtain multiple frames of mineral images.

[0027] Preferably, the image evaluation values ​​corresponding to the initial focus of the target area and the positions of its adjacent focal planes are read. The optimal focus position is predicted based on the trend of the image evaluation values. When the evaluation values ​​of both focal planes before and after the initial focus of the target area show a trend of gradually increasing towards the initial focus, the initial focus of the target area is taken as the starting position for fine-tuning. When there is a significant shift in the evaluation values ​​of adjacent focal planes, a slight shift correction is made to the initial focus of the target area based on the direction of evaluation value growth to obtain the starting position for fine-tuning. Subsequently, using the starting position for fine-tuning as the acquisition center, the focusing motor is driven to scan the focal plane according to a preset fine-tuning step size, preferably 2μm to 10μm. Corresponding images are acquired at each fine-tuning position, and the sharpness of the acquired images is evaluated using the aforementioned image evaluation function. A fine-tuning evaluation value sequence is generated; then, the fine-tuning evaluation value sequence is compared and analyzed to determine the focal plane position corresponding to the image with the largest evaluation value, and this focal plane position is determined as the fine-tuning focus of the target area; after obtaining the fine-tuning focus of the target area, a focus locking compensation mechanism is activated. By monitoring the changes in the evaluation values ​​of continuously acquired images in real time, it is determined whether the focus has drifted. When the evaluation value of three consecutive frames decreases by more than a preset threshold, it is determined that focus drift has occurred, and the focusing motor is driven to perform compensation search according to a fine-tuning step range before and after the current focus position, and the focal plane position with the largest evaluation value is repositioned as the locked focus; after the focus is locked, multiple frames of mineral images are continuously acquired with the locked focus as the center, wherein the multiple frames of mineral images are preferably 5 to 15 frames, thereby obtaining a multi-frame mineral image dataset for subsequent multi-frame fusion enhancement processing.

[0028] The multi-frame mineral images are subjected to multi-frame stacking noise reduction and edge enhancement to obtain high-definition mineral images, and imaging quality assessment and compensation optimization are performed based on the high-definition mineral images.

[0029] First, image registration is performed on the acquired multi-frame mineral images to eliminate image position differences caused by equipment micro-vibration or focus micro-shift. Then, the registered multi-frame mineral images are fused and denoised to reduce the impact of random noise and low-light noise. An edge enhancement algorithm is then used to strengthen details such as mineral grain boundaries, cleavage features, and mineral contact relationships, thereby generating high-definition mineral images. Next, the imaging quality of these high-definition mineral images is evaluated by calculating image sharpness, signal-to-noise ratio, edge strength, and brightness uniformity. Imaging quality evaluation results are generated based on the differences between each indicator and preset quality standards. When the evaluation result meets the preset quality requirements, the high-definition mineral image is stored and displayed as the final output result. When the imaging quality evaluation result does not meet the preset quality requirements, compensation optimization parameters are automatically generated according to the compensation rules corresponding to the unmet indicators. Specifically, when the sharpness indicator is lower than the preset threshold, the focus parameters are adjusted; when the signal-to-noise ratio indicator is lower than the preset threshold, the exposure time and image gain parameters are adjusted; when the edge intensity indicator is lower than the preset threshold, the edge enhancement parameters are adjusted; and when the brightness uniformity indicator is lower than the preset threshold, the light source brightness parameters are adjusted. Based on the compensation optimization parameters, image acquisition or image enhancement processing is re-executed to complete the closed-loop optimization of imaging quality.

[0030] Furthermore, multi-frame stacking noise reduction and edge enhancement are performed on the multi-frame mineral images to obtain high-resolution mineral images, including: The evaluation values ​​of the multiple mineral images are used to perform multi-frame weighted superposition and noise reduction to obtain multiple usable mineral images; the adaptive Canny edge detection algorithm is used to perform edge enhancement processing on the multiple usable mineral images to obtain high-definition mineral images.

[0031] First, the image evaluation value corresponding to each frame of the mineral image is read. The image evaluation value is calculated using the Laplacian variance evaluation function. Specifically, the Laplacian operator is first used to perform a convolution operation on the i-th frame of the mineral image to obtain the Laplacian response image, and then the corresponding image evaluation value is calculated according to the following formula: ,in, Let M be the evaluation value of the i-th frame of the mineral image, where M is the number of pixels in the width direction and N is the number of pixels in the height direction. The coordinates of the i-th frame mineral image after processing with the Laplacian operator. The response value at that location, This represents the average response value of the corresponding Laplacian response image; a higher evaluation value indicates clearer mineral edges and texture details, corresponding to higher image quality. Subsequently, fusion weights are calculated based on the evaluation values ​​corresponding to each mineral image frame. To enhance the contribution of high-quality images in the fusion process, an exponential weighting method is used to calculate the weighting coefficients corresponding to each mineral image frame: ,in, Let be the fusion weight corresponding to the i-th frame of the mineral image, and n be the total number of mineral images participating in the fusion. Then, image registration is performed on multiple frames of mineral images to align the mineral edges and texture features in each frame in a unified coordinate system. Finally, weighted superposition fusion is performed based on the corresponding weighting coefficients to obtain multiple usable mineral images. The calculation process is as follows: ,in, To merge the image in coordinates Pixel value at that location, For the i-th frame of the mineral image, in coordinates The pixel values ​​at each location are used; weighted fusion is applied to give higher-resolution mineral images a greater contribution ratio while suppressing the influence of random noise, thereby obtaining multiple usable mineral images. These multiple usable mineral images are then converted to grayscale images and smoothed using a Gaussian filter to remove residual noise; the mean grayscale value of the smoothed image is calculated. and grayscale standard deviation Based on the grayscale mean and grayscale standard deviation The threshold parameters of the Canny edge detection algorithm are adaptively determined, where the low threshold is set to... The high threshold is set to Finally, the adaptive Canny edge detection algorithm is used to extract mineral grain boundaries, mineral cleavage boundaries, and mineral contact interface contours. The extracted edge information is then fused and enhanced with multiple available mineral images to improve mineral edge contrast and texture detail clarity, thereby obtaining high-definition mineral images.

[0032] In summary, the embodiments of this application have at least the following technical effects: First, a portable rock thin section rapid analysis workstation was acquired. This workstation includes a lightweight body, a multi-optical mode integrated module, a high-definition imaging and sensing unit, an intelligent interactive terminal, an adjustable clamping mechanism, and a power supply unit. Next, the workstation was activated to collect field environmental data and perform parameter calibration to obtain initial imaging calibration parameters. Then, the rock thin section was fixed to the adjustable clamping mechanism, a target optical mode was selected and switched, and the camera was driven to adaptively focus and image the rock thin section based on the target optical mode and the initial imaging calibration parameters, acquiring multiple frames of mineral images. Finally, multi-frame superposition noise reduction and edge enhancement were performed on the multiple mineral images to obtain high-definition mineral images, and imaging quality assessment and compensation optimization were performed based on these high-definition mineral images. This method solves the technical problems of unstable imaging quality and poor mineral detail recognition in existing technologies for rock thin sections under field conditions, achieving the technical effect of improving the clarity and stability of rock thin section imaging.

[0033] Example 2, based on the same inventive concept as the adaptive imaging optimization method for rock thin sections applicable to field environments in the foregoing examples, such as... Figure 2 As shown, this application provides an adaptive imaging optimization system for rock thin sections suitable for field environments, wherein the system includes: Machine interaction component 11: Acquires a portable rock thin section rapid analysis workstation, which includes a lightweight body, a multi-optical mode integrated module, a high-definition imaging and sensing unit, an intelligent interactive terminal, an adjustable clamping mechanism, and a power supply unit; Parameter calibration component 12: Starts the portable rock thin section rapid analysis workstation to collect field environmental data and perform parameter calibration to obtain initial imaging calibration parameters; Adaptive focusing imaging component 13: Fixes the rock thin section to the adjustable clamping mechanism, selects and switches the target optical mode, and drives the camera to perform adaptive focusing imaging on the rock thin section based on the target optical mode and the initial imaging calibration parameters to acquire multiple frames of mineral images; Image processing component 14: Performs multi-frame superposition noise reduction and edge enhancement on the multiple frames of mineral images to obtain high-definition mineral images, and performs imaging quality evaluation and compensation optimization based on the high-definition mineral images.

[0034] Furthermore, the parameter calibration component 12 is used to perform the following method: An environment type determination rule is constructed, and the field environment data is used to determine the current environment type. Based on the field environment imaging measurement data, an environment-imaging parameter mapping model is preset. The environment-imaging parameter mapping model is called to calibrate the parameters of the current environment type to obtain the initial imaging calibration parameters.

[0035] Furthermore, the adaptive focusing imaging component 13 is used to perform the following method: The initial imaging calibration parameters are adjusted and compensated twice based on the target optical mode to obtain the target imaging calibration parameters; the camera is driven to automatically scan the rock thin section according to the target imaging calibration parameters to obtain a global rock preview image; mineral region identification and adaptive focusing imaging are performed on the global rock preview image to obtain multiple frames of mineral images.

[0036] Furthermore, the adaptive focusing imaging component 13 is used to perform the following method: Mineral regions are identified and their locations are marked on the global rock preview image to obtain a list of target mineral region coordinates; adaptive coarse focusing is performed based on the target mineral region coordinate list to determine the initial focus of the target region; fine focusing and multi-frame image acquisition are performed based on the initial focus of the target region to obtain multiple frames of mineral images.

[0037] Furthermore, the adaptive focusing imaging component 13 is used to perform the following method: Based on the target mineral region coordinate list, the focusing motor is driven to continuously acquire images according to a preset coarse adjustment step size to obtain a multi-frame region image set; an image evaluation function is selected, and the multi-frame region image set is evaluated according to the image evaluation function to determine a multi-frame image evaluation value set; based on the multi-frame image evaluation value set, adaptive coarse adjustment focusing is performed on the multi-frame region image set to determine the initial focus of the target region.

[0038] Furthermore, the adaptive focusing imaging component 13 is used to perform the following method: Based on the initial focus of the target area, the fine-tuning position is predicted to obtain the fine-tuning starting position; with the fine-tuning starting position as the acquisition center, multiple frames of images are acquired according to the preset fine-tuning step size and evaluated and filtered by the image evaluation function to determine the fine-tuning focus of the target area; based on the fine-tuning focus of the target area, focus locking compensation and multiple frame image acquisition are performed to obtain multiple frames of mineral images.

[0039] Furthermore, the image processing component 14 is used to perform the following methods: The evaluation values ​​of the multiple mineral images are used to perform multi-frame weighted superposition and noise reduction to obtain multiple usable mineral images; the adaptive Canny edge detection algorithm is used to perform edge enhancement processing on the multiple usable mineral images to obtain high-definition mineral images.

[0040] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An adaptive imaging optimization method for rock thin sections suitable for field environments, characterized in that, The method includes: A portable rock thin section rapid on-site analysis workstation is obtained, which includes a lightweight body, a multi-optical mode integrated module, a high-definition imaging and sensing unit, an intelligent interactive terminal, an adjustable clamping mechanism, and a power supply unit. The portable rock thin section field rapid analysis workstation was activated to collect field environmental data and perform parameter calibration to obtain initial imaging calibration parameters. The rock thin section is fixed to the adjustable clamping mechanism, the target optical mode is selected and switched, and the camera is driven to perform adaptive focusing imaging of the rock thin section based on the target optical mode and the initial imaging calibration parameters to acquire multiple frames of mineral images. The multi-frame mineral images are subjected to multi-frame stacking noise reduction and edge enhancement to obtain high-definition mineral images, and imaging quality assessment and compensation optimization are performed based on the high-definition mineral images.

2. The adaptive imaging optimization method for rock thin sections applicable to field environments as described in claim 1, characterized in that, The portable rock thin section field rapid analysis workstation is activated to collect field environmental data and perform parameter calibration to obtain initial imaging calibration parameters, including: Construct an environment type determination rule, and use the environment type determination rule to determine the type of the field environment data to obtain the current environment type; Based on field environmental imaging measurement data, a pre-defined environment-imaging parameter mapping model is established. The environment-imaging parameter mapping model is invoked to calibrate the parameters of the current environment type, thereby obtaining the initial imaging calibration parameters.

3. The adaptive imaging optimization method for rock thin sections applicable to field environments as described in claim 1, characterized in that, Based on the target optical mode and the initial imaging calibration parameters, the camera is driven to perform adaptive focusing imaging of the rock thin section, acquiring multiple frames of mineral images, including: The initial imaging calibration parameters are adjusted and compensated twice based on the target optical mode to obtain the target imaging calibration parameters. The camera is driven to automatically scan the rock thin section according to the target imaging calibration parameters to obtain a full-area rock preview image; Mineral region identification and adaptive focusing imaging are performed on the global rock preview image to obtain multiple frames of mineral images.

4. The adaptive imaging optimization method for rock thin sections applicable to field environments as described in claim 3, characterized in that, Mineral region identification and adaptive focusing imaging are performed on the global rock preview image to obtain multiple frames of mineral images, including: Mineral regions are identified and their locations are marked on the global rock preview image to obtain a list of target mineral region coordinates; Based on the target mineral region coordinate list, adaptive coarse focusing is performed to determine the initial focus of the target region; Based on the initial focus of the target area, fine-tuning and focusing processing and multi-frame image acquisition are performed to obtain multiple frames of mineral images.

5. The adaptive imaging optimization method for rock thin sections applicable to field environments as described in claim 4, characterized in that, Adaptive coarse focusing is performed based on the target mineral region coordinate list to determine the initial focus of the target region, including: Based on the target mineral region coordinate list, the focusing motor is driven to continuously acquire images according to a preset coarse adjustment step size, thereby obtaining a multi-frame region image set. Select an image evaluation function, evaluate the multi-frame region image set according to the image evaluation function, and determine the multi-frame image evaluation value set; Based on the multi-frame image evaluation value set, adaptive coarse focusing is performed on the multi-frame regional image set to determine the initial focus of the target region.

6. The adaptive imaging optimization method for rock thin sections applicable to field environments as described in claim 5, characterized in that, Based on the initial focus of the target area, fine-tuning and focusing processing are performed, and multiple frames of images are acquired to obtain multiple frames of mineral images, including: Based on the initial focus of the target area, the fine-tuning position is predicted to obtain the fine-tuning starting position; Using the fine-tuning starting position as the acquisition center, multiple frames of images are acquired according to the preset fine-tuning step size and evaluated and filtered by the image evaluation function to determine the fine-tuning focus of the target area; Focus locking compensation and multi-frame image acquisition are performed based on the fine-tuning of the focus in the target area to obtain multiple frames of mineral images.

7. The adaptive imaging optimization method for rock thin sections applicable to field environments as described in claim 1, characterized in that, The multi-frame mineral images are subjected to multi-frame stacking noise reduction and edge enhancement to obtain high-resolution mineral images, including: Multi-frame weighted superposition noise reduction is performed based on the evaluation values ​​of the multi-frame mineral images to obtain multiple usable mineral images; An adaptive Canny edge detection algorithm is used to perform edge enhancement processing on the multiple available mineral images to obtain high-resolution mineral images.

8. An adaptive imaging optimization system for rock thin sections suitable for field environments, characterized in that, The system is used to implement the adaptive imaging optimization method for rock thin sections applicable to field environments as described in any one of claims 1-7, the system comprising: Machine interaction component: A portable on-site rapid analysis workstation for rock thin sections, the portable on-site rapid analysis workstation for rock thin sections includes a lightweight body, a multi-optical mode integrated module, a high-definition imaging and sensing unit, an intelligent interactive terminal, an adjustable clamping mechanism and a power supply unit; Parameter calibration component: The portable rock thin section field rapid analysis workstation is activated to collect field environmental data and perform parameter calibration to obtain initial imaging calibration parameters; Adaptive focusing imaging component: The rock thin section is fixed to the adjustable clamping mechanism, the target optical mode is selected and switched, and the camera is driven to perform adaptive focusing imaging of the rock thin section based on the target optical mode and the initial imaging calibration parameters to acquire multiple frames of mineral images. Image processing component: Performs multi-frame superposition noise reduction and edge enhancement on the multi-frame mineral images to obtain high-definition mineral images, and performs imaging quality assessment and compensation optimization based on the high-definition mineral images.

Images