Negative illumination-based contour imaging method and system

Abstract

The present invention relates to the technical field of illumination-based contour imaging. Disclosed are a negative illumination-based contour imaging method and system, for use in solving the problem of poor final presentation effect of a negative image. According to the present invention, the working efficiency is significantly improved by automating and optimizing an image processing process. A user does not need to manually perform each optimization step, and can quickly obtain an optimized high-quality image by means of preset parameters and processes. The optimized image is stored in a lossless format, which means that no information is lost during image storage, thereby maintaining the highest image quality. By selecting an appropriate algorithm and adjustment parameters, the visual effect of the negative image can be significantly improved, thereby improving the readability and aesthetics of the image. The quality inspection ensures that the finally stored image meets predetermined quality standards, thereby improving data reliability and reproducibility.

Description

Negative Illumination Profiling Imaging Method and System Technical Field

[0001] This invention relates to the field of illumination profiling imaging technology, specifically to a method and system for negative illumination profiling imaging. Background Technology

[0002] Negative film is an image obtained after exposure and development. Its brightness is the opposite of the subject, and its colors are the complementary colors of the subject. Simply put, the image seen after the film is developed is an inverted image. It needs to be enlarged or printed into a photograph to become an image with the same colors as the scene photographed.

[0003] Existing technologies cannot enhance the contours and details of objects in negative images through specific lighting and processing methods, resulting in poor negative image quality. Furthermore, the following problems also exist when processing negatives:

[0004] 1. The sample was not illuminated using a more sophisticated illumination method, and the images were not fully processed after illumination, resulting in poor quality of the original images.

[0005] 2. The sample images were not effectively illuminated and fused, resulting in poor image presentation.

[0006] 3. The negative image was not further optimized and generated after imaging, resulting in poor image quality. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for negative illumination contour imaging, which significantly improves work efficiency through automation and optimization of the image processing workflow. Users do not need to manually perform each optimization step; instead, they can quickly obtain high-quality optimized images through preset parameters and processes. The optimized images are saved in a lossless format, meaning that no information is lost during saving, maintaining the highest image quality. By selecting appropriate algorithms and adjusting parameters, the visual effect of negative images can be significantly improved, thereby enhancing image readability and aesthetics. Quality checks ensure that the final saved image meets predetermined quality standards, thus improving data reliability and repeatability, and solving problems in the prior art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] Negative illumination contouring imaging methods include:

[0010] The sample is illuminated, and the illuminated sample is imaged through a sensor. The sample image is then preprocessed, including image denoising, contrast adjustment, and image enhancement.

[0011] The preprocessed sample image is converted into a negative image. The negative image is then illuminated to highlight image details. Finally, the illuminated image is fused with the original sample image.

[0012] Preferably, irradiating the sample includes:

[0013] Before irradiating the samples, the samples are selected.

[0014] Sample selection includes collecting samples within the scope of the research objective, and excluding samples outside the scope of the research objective by observing the samples with the naked eye or a low-power microscope.

[0015] The sample is subjected to characteristic analysis, which includes optical characteristic analysis and physical characteristic analysis.

[0016] After the characteristic analysis, the samples are prepared, including sample fixation, sectioning and staining.

[0017] After sample preparation is completed, sample testing is carried out. Before irradiating the sample, a small-scale irradiation imaging test is performed on the sample. The irradiation intensity, angle and time parameters are adjusted based on the results of the small-scale irradiation imaging test.

[0018] Samples were selected based on the results of the illumination imaging test.

[0019] Preferably, irradiating the sample further includes:

[0020] The selected sample is irradiated. The irradiation process includes:

[0021] The irradiation source is selected based on the type of sample and the required imaging resolution. Irradiation sources include visible light, ultraviolet light, and X-rays.

[0022] Irradiation parameters, including irradiation intensity, time, and angle, are set based on the characteristic analysis of the samples and the results of irradiation imaging tests.

[0023] Place the prepared sample on the irradiation platform, turn on the irradiation source, and irradiate the sample according to the set parameters. At the same time, closely monitor the irradiation situation during the irradiation process.

[0024] After irradiation, immediately turn off the irradiation source and conduct a preliminary examination of the irradiated sample with the naked eye or a low-power microscope;

[0025] The irradiated sample is transferred to the imaging sensor in preparation for sample imaging.

[0026] Furthermore, all irradiation parameters were recorded, including the type, intensity, time, and sample location of the irradiation source.

[0027] Preferably, the irradiated sample is imaged using a sensor, including:

[0028] Turn on the imaging sensor and preview the imaging effect of the sample on the imaging sensor in the software. Adjust the parameters until you get a satisfactory preview image. After confirming that all settings are correct, capture the image of the sample.

[0029] The captured sample images are quality checked, and the quality-checked sample images are saved in a lossless format. The imaging parameters, including exposure time, focal length, and resolution, are also saved along with the sample images.

[0030] Preferably, the sample image undergoes image preprocessing, including:

[0031] First, image denoising is performed on the sample image. Image denoising includes: analyzing the noise type of the sample image, which includes random noise and fixed pattern noise; selecting a denoising algorithm based on the noise type, which includes mean filtering, median filtering and wavelet transform denoising; and adjusting the parameters of the denoising algorithm based on the noise intensity and image details.

[0032] After image denoising is completed, contrast adjustment is performed. Contrast adjustment includes: evaluating the contrast of the denoised sample image, determining whether the sample image needs adjustment based on the evaluation result, and if adjustment is needed, using histogram equalization.

[0033] After contrast adjustment, image enhancement is performed, which includes: using a sharpening filter to enhance the image edges, and then adjusting the image colors.

[0034] The enhanced sample image is compared with the original pre-selected sample, and the preprocessing effect is evaluated based on the comparison results;

[0035] If the pretreatment effect is not within the acceptable range, repeat the above steps to make adjustments.

[0036] Preferably, converting the preprocessed sample image into a negative image includes:

[0037] The sample image was loaded using image processing software;

[0038] Read the loaded sample image, including the brightness value of each pixel;

[0039] Then invert the value of each pixel;

[0040] Save the inverted image; this will result in a negative image.

[0041] Preferably, the negative image is subjected to illumination clarification processing, including:

[0042] The key features in the negative image are identified, and then the purpose of the illumination clarification processing of the negative image is confirmed. The purpose of illumination clarification processing includes enhancing edges, increasing contrast, or highlighting specific structures.

[0043] The illumination contouring algorithm is selected according to the processing purpose. Illumination contouring algorithms include edge enhancement algorithms, local contrast enhancement algorithms, or wavelet-based contouring algorithms.

[0044] The changes in the negative image are previewed in real time during processing, and the algorithm parameters are adjusted based on the preview results;

[0045] Finally, the illumination and stenciling process of the negative image is completed.

[0046] Preferably, fusing the image after illumination profiling with the original sample image includes:

[0047] The resolution and size of the illuminated negative image and the original sample image are unified;

[0048] After unification, a multi-resolution fusion method is used for image fusion.

[0049] Image fusion includes: performing wavelet transform on the negative and the original image to decompose them into sub-images of different frequencies, selecting appropriate low-frequency and high-frequency components for fusion, and then performing inverse transform to obtain the fused image;

[0050] When performing image fusion, preview the fusion effect and adjust the fusion parameters, including weight and transparency, based on the preview results;

[0051] The final fused sample image is obtained.

[0052] A negative illumination contouring imaging system includes:

[0053] The fusion image optimization unit is used for:

[0054] The fused sample images are then optimized. The optimization process includes: contrast optimization, color balance, sharpening, noise reduction, brightness adjustment, detail enhancement, image cropping, and rotation.

[0055] Save the optimized image in a lossless format and record the parameters of all optimization steps;

[0056] The final optimized image is obtained.

[0057] Preferred, including:

[0058] Optimize the image generation unit for:

[0059] The optimized image is generated into an image; the generation parameters are determined before the image generation is performed.

[0060] The generation parameters include output format, image resolution, and color mode;

[0061] Once the generation parameters are confirmed, the optimized image is obtained.

[0062] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0063] 1. The negative illumination contour imaging method and system provided by the present invention adjusts the parameters of the denoising algorithm according to the intensity of noise and the details of the image. This ensures that the processing result removes noise while maintaining the clarity and details of the image. Quality checks ensure that the final saved image meets the predetermined quality standards, thereby improving the reliability and repeatability of the data. Adjusting the illumination parameters according to the test results ensures the accuracy of the illumination process and the optimization of the imaging effect.

[0064] 2. The negative illumination contour imaging method and system provided by the present invention adopts a multi-resolution fusion method, which can make full use of the information of the image at different scales, and helps to capture and fuse the detailed information in the image. By selecting appropriate algorithms and adjusting parameters, the visual effect of the negative image can be significantly improved, thereby enhancing the readability and aesthetics of the image. By reversing the brightness value, the originally bright areas become dark, and the dark areas become bright. This change can often reveal details that are not easily noticed in the original image.

[0065] 3. The negative illumination contour imaging method and system provided by this invention significantly improves work efficiency through automation and optimization of the image processing workflow. Users do not need to manually perform each optimization step, but can quickly obtain high-quality optimized images through preset parameters and processes. The optimized images are saved in a lossless format, which means that no information is lost during the saving process, maintaining the highest image quality. Attached Figure Description

[0066] Figure 1 is a schematic diagram of the negative film illumination contour imaging steps of the present invention;

[0067] Figure 2 is a schematic diagram of the negative illumination contour imaging process of the present invention. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] To address the problem in existing technologies where the sample is not illuminated using a more sophisticated method and further image processing is not performed on the incompletely illuminated images, resulting in poor original image quality (see Figures 1 and 2), this embodiment provides the following technical solution:

[0070] Negative illumination contouring imaging methods include:

[0071] The sample is illuminated, and the illuminated sample is imaged through a sensor. The sample image is then preprocessed, including image denoising, contrast adjustment, and image enhancement.

[0072] The preprocessed sample image is converted into a negative image. The negative image is then illuminated to highlight image details. Finally, the illuminated image is fused with the original sample image.

[0073] Specifically, image preprocessing steps (denoising, contrast adjustment, and enhancement) can significantly improve the overall image quality, making the image clearer and richer in detail, laying a good foundation for subsequent processing and analysis. Converting the sample image into a negative image and then using illumination contouring can further highlight the details in the image. Negation processing itself is an image inversion technique that can reveal information that is not easily perceived in the original image. Illumination contouring, on the other hand, enhances the lighting and shadow effects in the image through specific algorithms or techniques, making the details more vivid. Fusing the processed negative image with the original sample image can combine the information advantages of both. This fusion not only preserves the key information in the original image but also introduces the prominent details in the negative image, thereby improving the overall information content and readability of the image. Through a series of meticulous image processing steps, the image clarity and detail visibility can be significantly improved, thus helping professionals to more accurately diagnose and analyze samples. The entire processing flow can be automated by computer programs, reducing the tediousness and errors of manual operation. This not only improves processing efficiency but also ensures the consistency and stability of the processing results.

[0074] Irradiating the sample includes:

[0075] Before irradiating the samples, the samples are selected.

[0076] Sample selection includes collecting samples within the scope of the research objective, and excluding samples outside the scope of the research objective by observing the samples with the naked eye or a low-power microscope.

[0077] The sample is subjected to characteristic analysis, which includes optical characteristic analysis and physical characteristic analysis.

[0078] After the characteristic analysis, the samples are prepared, including sample fixation, sectioning and staining.

[0079] After sample preparation is completed, sample testing is carried out. Before irradiating the sample, a small-scale irradiation imaging test is performed on the sample. The irradiation intensity, angle and time parameters are adjusted based on the results of the small-scale irradiation imaging test.

[0080] Samples were selected based on the results of the illumination imaging test.

[0081] The selected sample is irradiated. The irradiation process includes:

[0082] The irradiation source is selected based on the type of sample and the required imaging resolution. Irradiation sources include visible light, ultraviolet light, and X-rays.

[0083] Irradiation parameters, including irradiation intensity, time, and angle, are set based on the characteristic analysis of the samples and the results of irradiation imaging tests.

[0084] Place the prepared sample on the irradiation platform, turn on the irradiation source, and irradiate the sample according to the set parameters. At the same time, closely monitor the irradiation situation during the irradiation process.

[0085] After irradiation, immediately turn off the irradiation source and conduct a preliminary examination of the irradiated sample with the naked eye or a low-power microscope;

[0086] The irradiated sample is transferred to the imaging sensor in preparation for sample imaging.

[0087] Furthermore, all irradiation parameters were recorded, including the type, intensity, time, and sample location of the irradiation source.

[0088] Specifically, the process begins with sample selection based on the research objective, ensuring the selected samples are closely related to the research topic, thus improving the research's focus and efficiency. Unsuitable samples are eliminated through visual inspection or low-power microscopy, ensuring initial sample quality. Sample characteristic analysis, including optical and physical properties, helps to more accurately understand sample properties, providing a foundation for subsequent irradiation and imaging. Sample preparation includes fixation, slicing, and staining; these steps help improve image clarity and contrast. Small-scale irradiation imaging tests are conducted before formal irradiation, and irradiation parameters are adjusted based on the test results, ensuring the accuracy of the irradiation process and optimization of imaging effects. Selecting appropriate irradiation sources based on sample type and required imaging resolution demonstrates the flexibility and adaptability of the approach. Setting and strictly controlling irradiation parameters ensures consistency and stability during irradiation. Close monitoring during irradiation allows for timely detection and handling of potential problems, guaranteeing the safety and effectiveness of the process. Immediate preliminary inspection after irradiation helps to identify and correct any potential issues, ensuring image quality. The irradiated samples are then transferred to an imaging sensor for imaging, providing a foundation for subsequent image analysis and research. Recording all irradiation parameters helps to trace and analyze the irradiation process, providing reference and basis for future research.

[0089] The irradiated sample is imaged using a sensor, including:

[0090] Turn on the imaging sensor and preview the imaging effect of the sample on the imaging sensor in the software. Adjust the parameters until you get a satisfactory preview image. After confirming that all settings are correct, capture the image of the sample.

[0091] The captured sample images are quality checked, and the quality-checked sample images are saved in a lossless format. The imaging parameters, including exposure time, focal length, and resolution, are also saved along with the sample images.

[0092] Specifically, before capturing an image, users can preview the sample imaging effect on the imaging sensor through software. This preview function allows users to adjust imaging parameters (such as exposure time, focal length, and resolution) in real time, ensuring optimal imaging results before image capture. Previewing and adjusting avoids spending significant time and resources on post-processing to correct image quality, improving work efficiency. Quality checking of captured sample images is a crucial step in ensuring image accuracy and integrity. This helps identify and correct any potential imaging problems, such as blur, underexposure, or overexposure. Quality checking ensures that the final saved image meets predetermined quality standards, thereby improving data reliability and repeatability. Saving quality-checked sample images in a lossless format ensures that no information or quality is lost during storage and transmission. Images saved in a lossless format have higher accuracy and reliability in subsequent analysis and processing, helping to avoid data loss or errors. Saving imaging parameters (including exposure time, focal length, and resolution) along with the sample image is essential for subsequent image analysis and processing. These imaging parameters provide detailed information about the image capture conditions, helping to interpret any anomalies or features in the image and enhancing data traceability and interpretability.

[0093] Image preprocessing of the sample images includes:

[0094] First, image denoising is performed on the sample image. Image denoising includes: analyzing the noise type of the sample image, which includes random noise and fixed pattern noise; selecting a denoising algorithm based on the noise type, which includes mean filtering, median filtering and wavelet transform denoising; and adjusting the parameters of the denoising algorithm based on the noise intensity and image details.

[0095] After image denoising is completed, contrast adjustment is performed. Contrast adjustment includes: evaluating the contrast of the denoised sample image, determining whether the sample image needs adjustment based on the evaluation result, and if adjustment is needed, using histogram equalization.

[0096] After contrast adjustment, image enhancement is performed, which includes: using a sharpening filter to enhance the image edges, and then adjusting the image colors.

[0097] The enhanced sample image is compared with the original pre-selected sample, and the preprocessing effect is evaluated based on the comparison results;

[0098] If the pretreatment effect is not within the acceptable range, repeat the above steps to make adjustments.

[0099] Specifically, by analyzing the noise type (random noise or fixed-pattern noise), the most suitable denoising algorithm (such as mean filtering, median filtering, or wavelet transform denoising) can be selected. This targeted processing can more effectively reduce noise while preserving important image details. Adjusting the parameters of the denoising algorithm according to the noise intensity and image details ensures that the processing result removes noise while maintaining image clarity and detail. Contrast evaluation and adjustment steps improve the visual effect of the image, making the information in the image clearer and easier to distinguish. Histogram equalization is an effective contrast enhancement technique suitable for various image types. By sharpening the image edges with a sharpening filter, it can enhance image details and structure. At the same time, color adjustment can correct color deviations in the image, improving the overall image quality. Comparing the preprocessed image with the original image allows for a direct evaluation of the preprocessing effect. This evaluation mechanism helps ensure the effectiveness of the preprocessing steps and allows for necessary adjustments. If the preprocessing effect is not within acceptable limits, the scheme allows for repeating the above steps for adjustment. This iterative optimization process ensures that the final image quality meets specific requirements.

[0100] To address the problem in existing technologies where sample images are not effectively illuminated and fused, resulting in poor image rendering, please refer to Figures 1 and 2. This embodiment provides the following technical solution:

[0101] Converting preprocessed sample images into negative images includes:

[0102] The sample image was loaded using image processing software;

[0103] Read the loaded sample image, including the brightness value of each pixel;

[0104] Then invert the value of each pixel;

[0105] Save the inverted image; this will result in a negative image.

[0106] Specifically, this method primarily relies on the basic functions of image processing software, such as loading images, reading pixel values, and saving images. Inverting pixel values ​​is also relatively simple, typically achieved through built-in functions in programming languages ​​or simple operations in image processing libraries. Because the processing of each pixel is independent and the operation (inverting brightness values) is relatively simple, the entire process can be very fast. This is particularly advantageous for applications requiring rapid processing of large numbers of images. Negative images have a strong visual contrast and are commonly used in artistic processing, scientific research, or medical diagnosis. By inverting brightness values, bright areas become darker, and dark areas become brighter. This change often reveals details that are not easily noticeable in the original image. It can be applied to various types of images, whether color or grayscale, to generate negative images by inverting pixel values. Furthermore, this method can be combined with other image processing techniques to further enrich image processing capabilities.

[0107] Illumination and highlighting of negative images includes:

[0108] The key features in the negative image are identified, and then the purpose of the illumination clarification processing of the negative image is confirmed. The purpose of illumination clarification processing includes enhancing edges, increasing contrast, or highlighting specific structures.

[0109] The illumination contouring algorithm is selected according to the processing purpose. Illumination contouring algorithms include edge enhancement algorithms, local contrast enhancement algorithms, or wavelet-based contouring algorithms.

[0110] The changes in the negative image are previewed in real time during processing, and the algorithm parameters are adjusted based on the preview results;

[0111] Finally, the illumination and stenciling process of the negative image is completed.

[0112] Specifically, the key features in the negative image are first identified, ensuring that subsequent processing optimizes the image for important information. Based on the processing objective (e.g., edge enhancement, contrast improvement, or highlighting specific structures), a specific illumination contouring algorithm is selected. This targeted processing more effectively achieves the desired visual effect. Multiple illumination contouring algorithms are available, including edge enhancement, local contrast enhancement, and wavelet-based contouring algorithms, increasing processing flexibility and allowing selection of appropriate algorithms based on different image characteristics and processing needs. During processing, image changes are previewed in real-time, and algorithm parameters are adjusted based on the preview results. This real-time feedback mechanism makes the processing more intuitive and controllable. By selecting appropriate algorithms and adjusting parameters, the visual effect of the negative image can be significantly improved, such as enhancing edge sharpness, increasing overall contrast, or highlighting specific structures, thereby improving image readability and aesthetics. From key feature identification to algorithm selection, parameter adjustment, and final processing completion, the entire process is well-organized, easy to understand, and easy to operate. The real-time preview function allows users to intuitively see the processing effect, simplifying the parameter adjustment process and reducing operational difficulty.

[0113] The image after illumination profiling is fused with the original sample image, including:

[0114] The resolution and size of the illuminated negative image and the original sample image are unified;

[0115] After unification, a multi-resolution fusion method is used for image fusion.

[0116] Image fusion includes: performing wavelet transform on the negative and the original image to decompose them into sub-images of different frequencies, selecting appropriate low-frequency and high-frequency components for fusion, and then performing inverse transform to obtain the fused image;

[0117] When performing image fusion, preview the fusion effect and adjust the fusion parameters, including weight and transparency, based on the preview results;

[0118] The final fused sample image is obtained.

[0119] Specifically, by unifying the resolution and size of the negative image and the original sample image, the fundamental consistency of the fusion process is ensured. This is a crucial prerequisite for image fusion, avoiding image distortion or poor fusion results caused by size or resolution mismatch. Employing a multi-resolution fusion method allows for full utilization of information at different scales. This method helps capture and fuse detailed information in the image, improving the sharpness and detail of the fused image. Wavelet transform is an effective image decomposition tool that can decompose an image into sub-images of different frequencies. This step allows for finer control over low-frequency and high-frequency components, thereby optimizing the fusion effect. Low-frequency components typically represent the main structure and contours of the image, while high-frequency components contain details and texture information. The fusion effect is previewed during the fusion process, and parameters such as weights and transparency are adjusted based on the preview results. This flexibility ensures that the final fused image meets specific visual needs and application requirements. Through these steps, the final fused image combines the image information after illumination profiling and the features of the original sample image, thereby enhancing certain aspects of the image (such as sharpness and contrast) while preserving the original image information. This helps improve the overall quality of the image, making it more suitable for subsequent analysis, processing, or display.

[0120] To address the problem of poor image quality in existing technologies due to the lack of further image optimization and generation of the post-imaging negative image, please refer to Figures 1 and 2. This embodiment provides the following technical solution:

[0121] A negative illumination contouring imaging system includes:

[0122] The fusion image optimization unit is used for:

[0123] The fused sample images are then optimized. The optimization process includes: contrast optimization, color balance, sharpening, noise reduction, brightness adjustment, detail enhancement, image cropping, and rotation.

[0124] Save the optimized image in a lossless format and record the parameters of all optimization steps;

[0125] The final optimized image is obtained.

[0126] Optimize the image generation unit for:

[0127] The optimized image is generated into an image; the generation parameters are determined before the image generation is performed.

[0128] The generation parameters include output format, image resolution, and color mode;

[0129] Once the generation parameters are confirmed, the optimized image is obtained.

[0130] Specifically, the image optimization unit performs multifaceted optimizations on the image, including contrast optimization, color balance, sharpening, noise reduction, brightness adjustment, detail enhancement, image cropping, and rotation. These steps collectively ensure optimal visual performance, significantly improving clarity, color accuracy, and overall aesthetics. The optimized image is saved in a lossless format, meaning no information is lost during storage, maintaining the highest image quality. Recording parameters for all optimization steps facilitates subsequent traceability and reproduction of the image processing workflow, and allows for fine-tuning and comparison of processing effects. The optimized image generation unit allows for the determination of generation parameters before image generation, including output format, image resolution, and color mode. This flexibility ensures that the generated image can be adjusted according to different needs and application scenarios, meeting diverse usage requirements. By optimizing the image generation unit, the final output image achieves optimal quality. Whether for printing, online publishing, or other purposes, it provides clear, accurate, and vibrant image results, significantly improving work efficiency through automated and optimized image processing workflows. Users do not need to manually perform each optimization step. Instead, they can quickly obtain high-quality optimized images through preset parameters and processes. By recording and saving the parameters of the optimization steps, this solution helps to standardize and regulate image processing. This helps ensure the consistency and comparability of images processed at different times and by different people.

[0131] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0132] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for negative illumination contour imaging, characterized in that, include: The sample is illuminated, and the illuminated sample is imaged through a sensor. The sample image is then preprocessed, including image denoising, contrast adjustment, and image enhancement. The preprocessed sample image is converted into a negative image. The negative image is then illuminated to highlight image details. Finally, the illuminated image is fused with the original sample image.

2. The negative illumination contour imaging method according to claim 1, characterized in that, Irradiating the sample includes: Before irradiating the samples, the samples are selected. Sample selection includes collecting samples within the scope of the research objective, and excluding samples outside the scope of the research objective by observing the samples with the naked eye or a low-power microscope. The sample is subjected to characteristic analysis, which includes optical characteristic analysis and physical characteristic analysis. After the characteristic analysis, the samples are prepared, including sample fixation, sectioning and staining. After sample preparation is completed, sample testing is carried out. Before irradiating the sample, a small-scale irradiation imaging test is performed on the sample. The irradiation intensity, angle and time parameters are adjusted based on the results of the small-scale irradiation imaging test. Samples were selected based on the results of the illumination imaging test.

3. The negative illumination contour imaging method according to claim 2, characterized in that, Irradiating the sample also includes: The selected sample is irradiated. The irradiation process includes: The irradiation source is selected based on the type of sample and the required imaging resolution. Irradiation sources include visible light, ultraviolet light, and X-rays. Irradiation parameters, including irradiation intensity, time, and angle, are set based on the characteristic analysis of the samples and the results of irradiation imaging tests. Place the prepared sample on the irradiation platform, turn on the irradiation source, and irradiate the sample according to the set parameters. At the same time, closely monitor the irradiation situation during the irradiation process. After irradiation, immediately turn off the irradiation source and conduct a preliminary examination of the irradiated sample with the naked eye or a low-power microscope; The irradiated sample is transferred to the imaging sensor in preparation for sample imaging. Furthermore, all irradiation parameters were recorded, including the type, intensity, time, and sample location of the irradiation source.

4. The negative illumination contour imaging method according to claim 1, characterized in that, The irradiated sample is imaged using a sensor, including: Turn on the imaging sensor and preview the imaging effect of the sample on the imaging sensor in the software. Adjust the parameters until you get a satisfactory preview image. After confirming that all settings are correct, capture the image of the sample. The captured sample images are quality checked, and the quality-checked sample images are saved in a lossless format. The imaging parameters, including exposure time, focal length, and resolution, are also saved along with the sample images.

5. The negative illumination contour imaging method according to claim 1, characterized in that, Image preprocessing of the sample images includes: First, image denoising is performed on the sample image. Image denoising includes: analyzing the noise type of the sample image, which includes random noise and fixed pattern noise; selecting a denoising algorithm based on the noise type, which includes mean filtering, median filtering and wavelet transform denoising; and adjusting the parameters of the denoising algorithm based on the noise intensity and image details. After image denoising is completed, contrast adjustment is performed. Contrast adjustment includes: evaluating the contrast of the denoised sample image, determining whether the sample image needs adjustment based on the evaluation result, and if adjustment is needed, using histogram equalization. After contrast adjustment, image enhancement is performed, which includes: using a sharpening filter to enhance the image edges, and then adjusting the image colors. The enhanced sample image is compared with the original pre-selected sample, and the preprocessing effect is evaluated based on the comparison results; If the pretreatment effect is not within the acceptable range, repeat the above steps to make adjustments.

6. The negative illumination contour imaging method according to claim 1, characterized in that, Converting preprocessed sample images into negative images includes: The sample image was loaded using image processing software; Read the loaded sample image, including the brightness value of each pixel; Then invert the value of each pixel; Save the inverted image; this will result in a negative image.

7. The negative illumination contour imaging method according to claim 1, characterized in that, Illumination and highlighting of negative images includes: The key features in the negative image are identified, and then the purpose of the illumination clarification processing of the negative image is confirmed. The purpose of illumination clarification processing includes enhancing edges, increasing contrast, or highlighting specific structures. The illumination contouring algorithm is selected according to the processing purpose. Illumination contouring algorithms include edge enhancement algorithms, local contrast enhancement algorithms, or wavelet-based contouring algorithms. The changes in the negative image are previewed in real time during processing, and the algorithm parameters are adjusted based on the preview results; Finally, the illumination and stenciling process of the negative image is completed.

8. The negative illumination contour imaging method according to claim 7, characterized in that, The image after illumination profiling is fused with the original sample image, including: The resolution and size of the illuminated negative image and the original sample image are unified; After unification, a multi-resolution fusion method is used for image fusion. Image fusion includes: performing wavelet transform on the negative and the original image to decompose them into sub-images of different frequencies, selecting appropriate low-frequency and high-frequency components for fusion, and then performing inverse transform to obtain the fused image; When performing image fusion, preview the fusion effect and adjust the fusion parameters, including weight and transparency, based on the preview results; The final fused sample image is obtained.

9. A negative illumination profiling imaging system, applied in the negative illumination profiling imaging method as described in claim 8, characterized in that, include: The fusion image optimization unit is used for: The fused sample images are then optimized. The optimization process includes: contrast optimization, color balance, sharpening, noise reduction, brightness adjustment, detail enhancement, image cropping, and rotation. Save the optimized image in a lossless format and record the parameters of all optimization steps; The final optimized image is obtained.

10. The negative illumination contour imaging system according to claim 9, characterized in that, include: Optimize the image generation unit for: The optimized image is generated into an image; the generation parameters are determined before the image generation is performed. The generation parameters include output format, image resolution, and color mode; Once the generation parameters are confirmed, the optimized image is obtained.

Images