Region-based imaging correction method and system

Abstract

The invention belongs to the field of near-eye light field display equipment, and discloses a region-based imaging correction method and system, and the method comprises the steps: obtaining an original display image from the near-eye light field display equipment, and carrying out the preprocessing of the original image; dividing the display image into a plurality of sub-regions according to the structure of the lens array; distortion analysis is carried out on each sub-region, and geometric and chromatic aberration distortion characteristics of the region are identified; creating a distortion model for each sub-region based on a distortion analysis result; designing a correction algorithm suitable for each sub-region to correct geometric and chromatic aberration distortion of the sub-region; applying the correction algorithm to each sub-region of the original display image to generate a corrected image; and loading the corrected image back to the near-eye light field display device for display. The method has strong robustness, can cope with errors caused by mechanical errors, is wide in applicability, is suitable for various different near-eye light field display devices, and provides reliable technical support for improving image quality and user experience.

Description

Technical Field

[0001] This invention belongs to, but is not limited to, the field of near-eye light field display technology. It proposes a method for resolving imaging errors in near-eye imaging based on lens arrays and lens array combinations. The invention is particularly applicable to lens arrays and lens array combinations, but is also applicable to other imaging systems. In particular, it relates to a region-based imaging correction method and system. Background Art

[0002] Near-eye light field display devices are a new type of display technology for virtual reality (VR) and augmented reality (AR), as well as XR (Extended Reality) and MR (Mixed Reality). They use lens arrays to generate high-quality light field images, providing users with a realistic visual experience. These display devices generate images with depth information, allowing users to perceive a three-dimensional effect. However, traditional near-eye light field display device designs have some defects and shortcomings, which affect image quality, mainly manifested as optical distortion. Due to the different imaging characteristics of each lens in the lens array, different areas of the image will experience different degrees of distortion. Common distortions include geometric distortion (such as barrel distortion and pincushion distortion) and chromatic aberration (such as dispersion). These distortions will cause the final displayed image to be blurry, affecting the user's visual experience.

[0003] Limitations of global correction methods: Traditional global correction methods attempt to uniformly correct distortion across the entire image. However, due to the varying distortion characteristics of each region, this approach cannot effectively eliminate local distortion. Global correction often only partially corrects certain types of distortion and fails to account for subtle differences across all regions.

[0004] Lack of regional correction methods: Currently, there is little research on regional correction methods for near-eye light field display devices. Since the distortion characteristics of each lens or region are different, it is difficult to achieve the ideal correction effect by adopting a unified correction strategy. Therefore, a more sophisticated regional correction method is needed to correct the specific distortion characteristics of each region to improve the clarity and visual effect of the overall image. Decreased user experience: Due to the limitations of the above-mentioned optical distortion and global correction methods, users often feel unclear images, visual fatigue, and even dizziness when using near-eye light field display devices. This seriously affects the actual effect and user satisfaction of near-eye light field display devices in VR and AR applications.

[0005] High technical difficulty: Currently, achieving accurate regional distortion correction faces technical challenges. Individually analyzing and correcting each lens or region requires complex algorithms and high-precision hardware support. This increases the difficulty and cost of technical implementation in practical applications.

[0006] In view of the above analysis, the technical problems that need to be solved urgently in the existing technology are:

[0007] Existing technologies have significant defects and deficiencies in distortion correction for near-eye light field display devices, and are limited by optical distortion and global correction methods. Summary of the Invention

[0008] In response to the problems existing in the prior art, the present invention provides a region-based imaging correction method and system.

[0009] The present invention is implemented as follows: a region-based imaging correction method, characterized in that the region-based imaging correction method specifically includes:

[0010] S1: Image acquisition and preprocessing: acquiring the original display image from the near-eye light field display device and preprocessing the original image;

[0011] S2: Region division: Divide the displayed image into multiple sub-regions based on the structure of the lens array; the present invention calculates which pixels in the pixel area corresponding to each lens have light entering the human eye through the corresponding lens and which pixels have not entered the human eye, then counts the areas of pixels that have entered the human eye, and corrects the image based on known feature points. The pixel areas that have not entered the human eye need to be filled using fitting or estimation methods;

[0012] S3: Distortion analysis: Perform distortion analysis on each sub-region to identify the geometric and chromatic distortion characteristics of the region;

[0013] S4: Distortion model creation: based on the distortion analysis results, a distortion model is created for each sub-region;

[0014] S5: Correction algorithm design: design a correction algorithm suitable for each sub-region to correct its geometric and chromatic aberration distortion;

[0015] S6: Image correction, applying the correction algorithm to each sub-region of the original display image to generate a corrected image;

[0016] S7: Image update, loading the corrected image back into the near-eye light field display device for display.

[0017] Furthermore, S1 obtains an original display image from a lens array near-eye light field display device. The image is generated by a lens array, and each lens is responsible for imaging a different sub-area. The image quality of these sub-areas varies due to different lens characteristics; the original image is preprocessed, including denoising, adjusting brightness and contrast, etc.

[0018] Furthermore, in S2, each sub-region corresponds to a lens, and the division method can be performed according to the geometric layout of the lens array, such as a grid or honeycomb pattern; a set of feature points is extracted from each sub-region as a basis for distortion analysis;

[0019] The S2 specifically includes:

[0020] (1) Identification of edge lens areas: First, identify the pixel areas corresponding to each lens, especially the edge lens areas; the light from many pixels in these areas cannot enter the pupil at the same time and therefore cannot be observed by the human eye;

[0021] (2) Light Visibility Analysis: Calculate which pixels have light entering the human eye through the corresponding lens. This can be determined by factors such as pupil position and lens optical properties. Determine which areas of pixels are visible and which are invisible. Quantify and calculate the amount of light entering the eye for each pixel. Perform subsequent calculations based on this amount.

[0022] (3) Image correction: Correct the pixel areas that enter the human eye; based on known feature points, you can adjust the image in these areas to compensate for distortion or other image quality issues;

[0023] (4) Filling and estimation: For pixel areas that are not visible to the human eye, use fitting or estimation algorithms to fill these areas; these algorithms can generate reasonable image data based on information from adjacent areas, thereby maintaining the consistency and integrity of the image. Further, the specific analysis methods in S3 include but are not limited to:

[0024] (1) Geometric distortion analysis: By detecting the deformation of straight lines and geometric shapes in the sub-area, barrel distortion, pincushion distortion, etc. are identified;

[0025] (2) Chromatic aberration analysis: Identify chromatic aberration problems caused by lens dispersion by detecting the shift of color in space.

[0026] Furthermore, in S4, based on the distortion analysis results, a distortion model is created for each sub-region, including a geometric distortion model and a chromatic aberration distortion model. The specific model creation method is as follows:

[0027] (1) Geometric distortion model: Use a polynomial (such as a cubic polynomial) to fit the geometric distortion of the sub-region and obtain the distortion parameters;

[0028] (2) Chromatic aberration distortion model: The chromatic aberration distortion of the sub-region is fitted using the chromatic aberration compensation method to obtain the chromatic aberration correction parameters.

[0029] Furthermore, in S5, a correction algorithm is designed for each sub-region to correct its geometric and chromatic aberration distortion. The specific algorithm design is as follows:

[0030] (1) Geometric distortion correction algorithm: Use the inverse transformation method to correct the barrel or pincushion distortion to the standard geometric shape. Common methods include using the distortion parameters to perform inverse polynomial transformation;

[0031] (2) Chromatic aberration distortion correction algorithm: Through color adjustment and gamma correction methods, the color shift caused by chromatic aberration is compensated to ensure accurate color reproduction.

[0032] Furthermore, in step S6, a correction algorithm is applied to each sub-region of the original display image to generate a corrected image. The specific steps are as follows:

[0033] (1) Applying a geometric distortion correction algorithm to each sub-region to generate a geometrically corrected sub-region image;

[0034] (2) Applying a chromatic aberration distortion correction algorithm to the geometrically corrected sub-region image to generate a final corrected sub-region image.

[0035] Another object of the present invention is to provide a region-based imaging correction system, the system specifically comprising:

[0036] An image acquisition and preprocessing module is used to acquire the original display image from the near-eye light field display device and perform preprocessing on the original image by denoising, adjusting brightness and contrast;

[0037] A region division module is used to divide the displayed image into multiple sub-regions according to the structure of the lens array, and calculate the pixel area that enters the human eye in the pixel area corresponding to each lens;

[0038] The distortion analysis module is used to perform geometric and chromatic aberration distortion analysis on each sub-region and identify the distortion characteristics of the region;

[0039] A distortion model creation module is used to create a geometric distortion model and a chromatic aberration distortion model for each sub-region based on the distortion analysis results;

[0040] Correction algorithm design module, used to design geometric distortion correction algorithm and chromatic aberration correction algorithm applicable to each sub-region;

[0041] an image correction module, configured to apply a correction algorithm to each sub-region of the original displayed image to generate a corrected image;

[0042] The image update module is used to load the corrected image back to the near-eye light field display device for display.

[0043] Furthermore, the area division module specifically includes:

[0044] an edge lens area identification unit, for identifying the pixel area corresponding to each lens, in particular the edge lens area;

[0045] A light visibility analysis unit, used to calculate which pixels' light enters the human eye through the corresponding lens, and determine the pixel areas that enter the human eye and those that do not;

[0046] An image correction unit, used to correct pixel areas entering the human eye and adjust the images of these areas based on known feature points;

[0047] The filling and estimation unit is used to fill the pixel areas that are not visible to the human eye using fitting or estimation methods to maintain the consistency and integrity of the image.

[0048] Furthermore, the distortion analysis module specifically includes:

[0049] A geometric distortion analysis unit, configured to identify barrel distortion and pincushion distortion by detecting the deformation of straight lines and geometric shapes within a sub-region;

[0050] The color difference analysis unit is used to identify the color difference problem caused by lens dispersion by detecting the color shift in space.

[0051] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0052] First, this invention is dedicated to significantly improving image clarity. Its core innovation lies in the meticulous division of the displayed image into multiple sub-regions, and in-depth imaging analysis and correction of each sub-region. This imaging analysis is not limited to distortion, but also comprehensively considers multiple factors that affect image quality, such as dispersion, speckle, color uniformity, and brightness uniformity.

[0053] Compared with traditional global correction methods, this technology has achieved significant progress. Global methods often find it difficult to capture subtle distortion imaging errors in all areas, while the regional correction strategy of the present invention can accurately lock in and correct specific imaging problems in each sub-area. This targeted processing method ensures that the imaging errors in each area can be effectively resolved, thereby greatly improving the clarity of the overall image. More importantly, this method has a wide range of applicability, and all types of imaging errors can be corrected through this approach. This means that no matter what the reason for the image quality degradation, it can be significantly improved through the technical means of the present invention. Ultimately, users will be able to enjoy an unprecedented clear and realistic visual experience, which marks a major leap in image processing technology.

[0054] While correcting optical imaging errors, the method of this invention can also correct imaging problems caused by mechanical errors (mechanical errors refer to system errors introduced during the production and installation of accessories). The process described below is static imaging error correction. This method is highly robust. Using eye tracking technology combined with the static imaging error correction proposed in this invention can achieve dynamic error correction, improving image quality at all viewing angles.

[0055] The present invention can handle special imaging problems in different areas and effectively utilize the image information that can be provided by each lens.

[0056] This invention reduces chromatic aberration by analyzing chromatic aberration in each subregion, creating a model for chromatic aberration and distortion errors, and designing a correction algorithm to compensate for and adjust these errors. Traditional methods have limited effectiveness in correcting these errors. This invention, by individually correcting these errors for each subregion, achieves more accurate color reproduction, reduces the visual distraction caused by these errors, and improves image color reproduction and visual comfort.

[0057] This invention enhances the overall visual quality of an image: by combining corrections for geometric and chromatic distortion, the correction of each sub-region is more comprehensive and detailed. Traditional correction methods often address only geometric or chromatic distortion. This integrated correction significantly improves the overall visual quality of the image, providing users with a more realistic and detailed visual experience.

[0058] This invention offers personalized correction for improved accuracy: A personalized geometric and chromatic aberration distortion model is created for each sub-region, and a corresponding correction algorithm is designed to precisely correct the specific distortion in each region. Because each lens has different imaging characteristics, personalized correction can precisely adjust to each distortion characteristic, significantly improving correction accuracy and effectiveness while avoiding the limitations of global correction methods.

[0059] This invention improves the user experience: through an effective regional correction method, image distortion is reduced, image clarity and color reproduction are improved, and the user's visual experience is enhanced. When using near-eye light field display devices, users can experience clearer and more realistic images, reduce visual fatigue and dizziness, and significantly improve user comfort and satisfaction.

[0060] This invention has broad applicability and strong scalability: The method is not only applicable to specific near-eye light field display devices, but can also be extended to display devices of varying sizes and configurations. This method has good adaptability and scalability, can be adjusted to suit different display devices, and is widely applicable to various near-eye display technologies, demonstrating strong market adaptability and commercial value.

[0061] This invention offers low cost and a simple structure: It uses software algorithms to correct errors such as chromatic aberration and distortion, eliminating the need for complex hardware modifications or the addition of additional hardware components. Compared to correction methods that require complex hardware support, this method relies primarily on software algorithms, reducing implementation cost and complexity and facilitating large-scale application and promotion.

[0062] This invention offers high production quality: Based on precise distortion analysis and correction models, it improves the accuracy and consistency of image correction. Through high-precision correction algorithms, it ensures the consistency of each device during production and use, improving product quality and user satisfaction.

[0063] In summary, the present invention effectively overcomes the shortcomings of the existing technology through regional division and refined correction of lens array near-eye light field display devices, improves image clarity and visual effects, enhances user experience, and has wide applicability and strong market competitiveness.

[0064] Second, as auxiliary evidence for the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0065] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0066] First, improve image quality

[0067] The present invention can significantly improve the image quality of near-eye light field display devices through a region-based imaging correction method and system. The specific benefits are as follows:

[0068] Improved image clarity: Distortion correction is performed individually for each sub-area, effectively eliminating geometric and chromatic aberration distortions, making the image clearer and sharper.

[0069] Enhanced color reproduction: Through precise color difference correction, the color distortion caused by color difference is reduced, ensuring accurate color reproduction and improving the visual experience.

[0070] Overall visual effect optimization: Comprehensive geometric and chromatic aberration correction makes the overall visual effect of the image more realistic and delicate.

[0071] Second, improve user experience

[0072] The area-based imaging correction method enables users to obtain a more comfortable visual experience when using near-eye light field display devices. The specific benefits are as follows:

[0073] Reduce visual fatigue: Clear and accurate images reduce user visual fatigue and improve comfort when using the device for long periods of time.

[0074] Reduce dizziness: Effective distortion correction reduces image deformation and reduces dizziness caused during use.

[0075] Enhanced satisfaction: Higher-quality images and better user experience will increase user satisfaction with the device, thereby increasing the market competitiveness of the device.

[0076] Third, expand the scope of application

[0077] The method of the present invention is not only applicable to specific near-eye light field display devices, but can also be extended to display devices of different sizes and configurations, and has a wide range of applicability. The specific benefits are as follows:

[0078] Strong adaptability: It can be adjusted according to different display devices and is suitable for various near-eye display technologies, broadening the application scenarios.

[0079] Large market potential: Due to its wide applicability, the present invention has strong market adaptability and commercial value and can be promoted and applied in multiple fields.

[0080] Fourth, reduce costs and complexity

[0081] Compared with the correction method that requires complex hardware support, the present invention mainly relies on software algorithm implementation, which has the advantages of low cost and simple structure. The specific benefits are as follows:

[0082] Reduced implementation costs: Error correction such as chromatic aberration and distortion is achieved through software algorithms, eliminating the need for complex hardware modifications or the addition of additional hardware components, reducing production and implementation costs.

[0083] Easy to apply on a large scale: The simple structure and low cost make the correction method of the present invention easy to apply and promote on a large scale in various near-eye light field display devices.

[0084] 5. Improve production quality and consistency

[0085] Through high-precision calibration algorithms, this invention can ensure the consistency of each device during production and use, thereby improving product quality. Specific benefits are as follows:

[0086] Strong consistency: Based on precise distortion analysis and correction models, the accuracy and consistency of image correction are improved, ensuring consistent display effects across different devices.

[0087] High user satisfaction: Consistent high-quality display effects improve user satisfaction and increase product market recognition.

[0088] The technical solution of this invention not only solves key problems in existing technologies but also offers significant expected benefits and commercial value. By improving image quality, enhancing user experience, expanding the scope of applications, reducing costs and complexity, and improving production quality and consistency, this invention has broad market prospects and enormous commercial value in the fields of virtual reality and augmented reality.

[0089] Commercial value:

[0090] First, market competitive advantage

[0091] 1. Enhanced Product Competitiveness: The technical solution of this invention significantly improves the image quality and user experience of near-eye display devices, making them more competitive in the market. High definition and excellent color reproduction will become the device's significant selling points, attracting more high-end users, especially in the high-end market of VR and AR devices, giving them a strong position.

[0092] 2. Enhanced Brand Value: High-quality images and a comfortable user experience will significantly increase user satisfaction and loyalty, thereby enhancing the brand's market recognition and reputation. This increased brand value will bring long-term commercial benefits to the company, including an expansion of the user base and an increase in market share.

[0093] Second, expand market applications

[0094] 1. Diversified Market Opportunities: The broad applicability of this invention's technical solution extends beyond VR and AR devices and can be extended to a variety of fields, including medical imaging, military simulation, and education and training. This will open up new market opportunities for companies, increase revenue streams, and enhance their overall market competitiveness.

[0095] 2. Innovative Application Areas: The exploration and application of high-precision image correction technology in emerging application areas will bring more business cooperation and innovation opportunities to enterprises. By continuously expanding the application scenarios of technology, enterprises can capture more market share and achieve diversified development.

[0096] Third, reduce production and maintenance costs

[0097] 1. Low Cost, High Return: This invention primarily relies on software algorithms for image correction, eliminating the need for complex hardware modifications or additional hardware components, resulting in low implementation costs. Device manufacturers can significantly improve product image quality without increasing hardware costs, thus achieving a low-cost, high-return business model.

[0098] 2. Simplify the production process: The technical solution has a simple structure, is easy to integrate and implement, and is suitable for large-scale application. This will simplify the production process, improve production efficiency, reduce production and maintenance costs, and thus increase the profit margin of the enterprise.

[0099] Fourth, enhance user stickiness

[0100] 1. Improve user satisfaction: High-quality images and a comfortable user experience significantly increase user satisfaction. Satisfied users are more likely to continue using your product and recommend it to other potential users, thereby increasing user stickiness and promoting word-of-mouth communication.

[0101] 2. Increase user loyalty: Improved user satisfaction will increase user loyalty to the brand. Loyal users will not only continue to purchase the company's new products, but also choose other products or services, increasing the company's repeat sales and cross-selling opportunities.

[0102] Fifth, create technical standards

[0103] 1. Leading Industry Standards: This invention's technical solution offers significant advantages in image correction and display quality improvement. Companies can promote and apply this technology to lead the development of industry standards. This not only enhances their industry influence but also brings them more business opportunities and market influence.

[0104] 2. Patent protection and technological barriers: By applying for patent protection, the technical solution of the invention becomes the company's core intellectual property, forming a technological barrier to prevent imitation and infringement by competitors. This will provide strong protection for the company in the market competition and ensure its leading position in the industry.

[0105] The technical solution of this invention has significant commercial value in terms of improving product competitiveness, broadening market applications, reducing production costs, enhancing user stickiness, and establishing technical standards. This technical solution not only brings direct economic benefits to enterprises but also enhances their market position and brand value, possessing broad commercial prospects and enormous development potential.

[0106] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0107] While this technical solution aims to address imaging errors in lens array near-eye light field display technology, its applications extend far beyond this. The following is a summary of the expected benefits and commercial value of this technical solution in various fields, as well as the technological gaps it addresses, both domestically and internationally.

[0108] First, lens array near-eye light field display technology

[0109] 1. Imaging Error Correction: This invention effectively addresses imaging error issues in lens array near-eye light field displays through regional correction. Existing technologies cannot simultaneously achieve high precision and low cost. This technical solution significantly improves image quality without increasing hardware costs, providing strong technical support for the development and application of lens array near-eye light field displays, filling a technological gap in this field both domestically and internationally.

[0110] 2. Enhanced User Experience: In near-eye display devices, high-quality image display is directly related to the user experience. This invention significantly improves image clarity and color reproduction through innovative regional correction technology, providing users with a more comfortable and realistic visual experience when using the device, thereby enhancing the market competitiveness of the device.

[0111] Second, applications in other imaging fields

[0112] 1. Medical imaging: Medical imaging technology requires highly accurate imaging. The regional correction technology of this invention can be applied to medical imaging equipment, such as CT scanners and MRI devices, to help correct imaging errors, provide clearer and more accurate medical images, and assist doctors in diagnosis and treatment, filling a gap in existing medical imaging technology.

[0113] 2. Satellite Imaging: In satellite imaging, high-precision image correction is crucial for geographic information systems (GIS) and remote sensing. The technical solution of this invention can be applied to satellite imaging equipment to correct imaging distortion caused by optical errors, providing more accurate image data of the Earth's surface and promoting the development of remote sensing technology.

[0114] 3. Security Monitoring: Security monitoring equipment has strict requirements for image quality, especially in high-risk areas. The imaging error correction technology of this invention can significantly improve the image quality of surveillance cameras, helping to identify and analyze details in surveillance footage, and providing more reliable technical support for public safety.

[0115] 4. Industrial Inspection: In the field of industrial inspection, high-precision imaging technology is crucial for quality control and defect detection. The technical solution of this invention can be applied to industrial inspection equipment to correct imaging errors, improve inspection accuracy and efficiency, and facilitate quality control and process optimization in industrial production.

[0116] This invention not only fills a technological gap in lens array near-eye light field display technology at home and abroad, but also further expands the boundaries of imaging error correction technology through its wide application in other imaging fields:

[0117] 1. Comprehensive technical advantages: Existing imaging error correction technologies are often only targeted at specific application scenarios. The regional correction technology of the present invention is highly versatile and adaptable and can be widely used in various imaging devices, filling the gap in comprehensive imaging error correction technology at home and abroad.

[0118] 2. Innovative algorithm: The regional correction algorithm adopted in the present invention has significant advantages in accuracy and efficiency. Compared with the traditional global correction method, the present invention can more accurately correct local imaging errors and improve the overall image quality. It has created a new technical path and filled the deficiencies in the existing technology.

[0119] 3. Widespread Application: Through successful applications in various fields such as medicine, satellites, security, and industry, the technical solution of the present invention has demonstrated its wide application potential and commercial value, filling the gap in high-precision imaging error correction technology in various fields and promoting technological progress and development in related industries.

[0120] The technical solution of the present invention not only achieves a breakthrough in lens array near-eye light field display technology, filling a technological gap at home and abroad, but also further enhances the wide applicability and commercial value of imaging error correction technology through its application in other imaging fields.

[0121] First, domestic technology gap

[0122] 1. High-Precision Image Correction: Existing near-eye display devices in the domestic market still have certain deficiencies in image correction, particularly in the development of low-cost, high-efficiency solutions. This invention provides an image correction technology based on regional correction that significantly improves image quality without increasing hardware costs. This technology fills a gap in the domestic field of high-precision image correction and provides new technical support for the development of related industries.

[0123] 2. Low-cost, high-performance solution: The domestic market has a huge demand for low-cost, high-performance near-eye display devices, but existing solutions often rely on expensive hardware components. This invention uses software algorithms to achieve high-precision image correction and display quality improvement, which not only reduces production costs but also improves overall device performance, providing a cost-effective and high-performance solution for the domestic market.

[0124] Second, foreign technology gaps

[0125] 1. Innovative Regional Correction Method: While some high-end near-eye display devices in the international market have achieved high image quality, there is still room for improvement in regional correction technology. The regional correction method proposed in this invention significantly improves image clarity and color reproduction by processing images by region. This innovative technology is a global first, filling a technological gap in this field internationally and possessing high technical value and application potential.

[0126] 2. Integrated, low-cost solution: Most high-end near-eye display devices on the international market rely on complex hardware systems to achieve high-quality image display. This invention achieves this goal through software algorithms, significantly reducing production costs and system complexity. This technical solution not only improves device performance but also reduces manufacturing and maintenance costs, providing an integrated, low-cost solution for the international market with broad application prospects.

[0127] Third, technological leadership

[0128] 1. Enhanced User Experience: Through the technical solutions of this invention, users will experience higher-quality images and a more comfortable experience when using near-eye display devices. This not only improves user satisfaction but also lays a solid foundation for future development. The application of this technology will significantly enhance the market competitiveness of devices in both domestic and international markets.

[0129] 2. Leading Industry Standards: The significant advantages of this technical solution in image correction and display quality improvement give it the potential to become an industry standard. By promoting and applying this technology, companies can lead the development of the industry, set new technical standards, and enhance their influence and competitiveness in domestic and international markets.

[0130] The technical solution of this invention not only fills the technological gap in high-precision image correction and low-cost, high-performance solutions both domestically and internationally, but also brings significant technical and commercial value to the development of related industries by improving user experience and setting industry standards. The application of this technology will significantly promote technological advancements in near-eye display devices and promote rapid market development.

[0131] (3) The technical solution of the present invention solves the technical problems that people have been eager to solve but have never been able to solve successfully:

[0132] The present invention's regional correction technology uses the image center as a reference to correct distortion for all lenslets. Image distortion caused by imaging errors has long been a difficult technical challenge in lens array near-eye light field display technology. Existing technical solutions struggle to balance accuracy and cost, resulting in unsatisfactory image quality. This invention, through its innovative regional correction method, successfully achieves high-precision imaging error correction, significantly improving image clarity and color reproduction, and completely resolving this long-standing problem.

[0133] 1. Innovation of regional correction methods

[0134] 1. Image Center as Reference: Traditional global correction methods struggle to address complex distortions within lens arrays. This invention uses the image center as a reference, individually correcting distortion for each lenslet, making the correction process more precise and effective. This innovative reference selection method ensures accurate image center correction while providing more balanced correction in the peripheral areas.

[0135] 2. Personalized Correction Strategy: This invention employs a personalized correction strategy tailored to the distortion characteristics of different lenslets. The correction parameters of each lenslet are adjusted based on its specific position and imaging characteristics, avoiding the bias issues that arise with global correction methods. This personalized correction ensures overall image consistency and clarity.

[0136] 2. Solve long-term technical problems

[0137] 1. High-precision distortion correction: Traditional technologies often struggle to achieve high-precision distortion correction in lens arrays. The present invention's regional correction method achieves high-precision distortion correction through precise benchmark selection and personalized correction strategies, significantly improving image clarity and detail, and resolving a long-standing precision issue plaguing the industry.

[0138] 2. Feasibility of large-scale application: This invention achieves high-precision correction while employing an efficient algorithm and low-cost implementation scheme, making large-scale application possible. Compared with traditional high-cost and high-complexity correction schemes, this invention has significant advantages in cost control and implementation efficiency, providing a solid technical foundation for large-scale commercial application.

[0139] 3. Wide Range of Applications: In addition to lens array near-eye light field display technology, the present invention's regional correction method also has broad application prospects in other imaging fields. For example, medical imaging, satellite imaging, security monitoring, and industrial inspection can all achieve high-precision distortion correction, improving image quality and accuracy, and resolving long-standing imaging error issues in various fields.

[0140] In summary, the regional correction technology of the present invention performs personalized distortion correction on all small lenses based on the image center. This technology not only successfully solves the imaging error problem in lens array near-eye light field display technology, but also has broad application prospects. It fills a gap in domestic and international industry technology, achieves high-precision, low-cost imaging error correction, and breaks through a long-standing technical bottleneck.

[0141] (4) The technical solution of the present invention overcomes technical prejudice:.

[0142] In lens array near-eye light field display technology, the traditional global correction method has always been regarded as the mainstream, but it has obvious shortcomings in dealing with complex distortion problems.

[0143] The specific embodiment of the present invention in overcoming technical prejudice

[0144] 1. Subverting Traditional Correction Methods: Traditional global correction methods attempt to address distortion across the entire image at once. However, given the complex optical characteristics of lens arrays, this approach often fails to meet high-precision requirements. This invention introduces a groundbreaking approach to regional correction, changing the traditional one-size-fits-all approach. This fundamentally overcomes technical biases and improves correction effectiveness.

[0145] 2. Personalized Correction Strategy: This invention emphasizes the uniqueness of each lenslet, customizing correction parameters based on its specific position and imaging characteristics. This approach avoids the common bias issues associated with global correction, making correction more precise for each lenslet and truly achieving an overall improvement in image quality.

[0146] 3. Efficient and low-cost implementation path: Traditionally, high-precision imaging correction is inherently costly and complex. This invention, through innovative algorithm design and an efficient implementation path, successfully combines high-precision correction with low-cost implementation, overcoming the cost barrier inherent in technological prejudice and providing a viable solution for large-scale commercial applications.

[0147] 4. Broad Application Prospects: This invention is not only applicable to lens array near-eye light field display technology but also has broad application prospects in other imaging fields. For example, medical imaging, satellite imaging, security monitoring, and industrial inspection can all be used to achieve high-precision distortion correction. This method breaks the common perception that new methods are limited to specific application areas and broadens the scope of application of the technology.

[0148] The technical solution of this invention, through its innovative regional correction method, not only solves complex distortion problems that traditional global correction methods struggle to address, but also overcomes the contradiction between high-precision correction and high cost and complexity. This fundamentally breaks down technical prejudices and opens up new research and application directions. This breakthrough brings a new perspective and solution to the industry, with significant technical and commercial value. BRIEF DESCRIPTION OF THE DRAWINGS

[0149] Figure 1 is a flow chart of a region-based imaging correction method provided by an embodiment of the present invention;

[0150] Figure 2 is a schematic diagram of a near-eye light field display provided by an embodiment of the present invention;

[0151] Figure 3 is a module diagram of a region-based imaging correction system provided by an embodiment of the present invention;

[0152] Figure 4 This is a result of directly using the LUT to perform transformation according to an embodiment of the present invention;

[0153] Figure 5 is a global image provided by an embodiment of the present invention;

[0154] Figure 6 This is a display of the overall imaging effect provided by an embodiment of the present invention;

[0155] Figure 7 This is the display of regional correction results provided by the embodiment of the present invention;

[0156] Figure 8 is the lens array imaging result taken by a high-definition camera provided by an embodiment of the present invention;

[0157] Figure 9 It is a display of high-definition cameras and experimental equipment provided by the embodiments of the present invention;

[0158] Figure 10 This is a separate demonstration of the imaging effect of Lens 1 provided by an embodiment of the present invention, as well as the results of image correction using different methods;

[0159] Figure 11 This is a demonstration of the effects of the corresponding areas of the three lenses lens1-3 provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0160] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0161] like Figure 1 As shown, an embodiment of the present invention provides a region-based imaging correction method, which specifically includes:

[0162] S1: Image acquisition and preprocessing: acquiring the original display image from the near-eye light field display device and preprocessing the original image;

[0163] S2: Region division: Based on the structure of the lens array, the displayed image is divided into multiple sub-regions. Calculation is performed on the pixel area corresponding to each lens, which pixels have light entering the human eye through the corresponding lens, and which pixels have light not entering the human eye. The area of pixels that enter the human eye is then counted, and the image is corrected based on known feature points. The pixel areas that do not enter the human eye need to be filled using fitting or estimation methods.

[0164] S3: Distortion analysis: Perform distortion analysis on each sub-region to identify the geometric and chromatic distortion characteristics of the region;

[0165] S4: Distortion model creation: based on the distortion analysis results, a distortion model is created for each sub-region;

[0166] S5: Correction algorithm design: design a correction algorithm suitable for each sub-region to correct its geometric and chromatic aberration distortion;

[0167] S6: Image correction, applying the correction algorithm to each sub-region of the original display image to generate a corrected image;

[0168] S7: Image update, loading the corrected image back into the near-eye light field display device for display.

[0169] Said S1, obtains the original display image from the lens array near-eye light field display device. The principle of near-eye light field display is as follows Figure 2 As shown in Figure 1. This image is generated by a lens array, with each lens responsible for imaging a different subregion. The image quality of these subregions varies due to the different lens characteristics. Preprocessing of the original image includes denoising and adjusting brightness and contrast.

[0170] In S2, each sub-region corresponds to a lens. The division method can be based on the geometric layout of the lens array, such as grid or honeycomb. A set of feature points is extracted from each sub-region as the basis for distortion analysis. The purpose of regional division is to perform separate distortion analysis and correction for each lens imaging sub-region to achieve more accurate correction results.

[0171] The S2 specifically includes:

[0172] (1) Identification of edge lens areas: First, identify the pixel areas corresponding to each lens, especially the edge lens areas; the light from many pixels in these areas cannot enter the pupil at the same time and therefore cannot be observed by the human eye;

[0173] (2) Light visibility analysis: Calculate which pixels’ light enters the human eye through the corresponding lens. This can be determined by factors such as pupil position and lens optical properties; determine which areas of pixels are pixels that enter the human eye and which are pixels that do not enter the human eye;

[0174] (3) Image correction: Correct the pixel areas that enter the human eye; based on known feature points, you can adjust the image in these areas to compensate for distortion or other image quality issues;

[0175] (4) Filling and estimation: For pixel areas that do not enter the human eye, use fitting or estimation methods to fill these areas; these algorithms can generate reasonable image data based on the information of adjacent areas, thereby maintaining the consistency and integrity of the image.

[0176] Light visibility analysis is a process that deeply examines how light passes through the lens and enters the human eye. It requires not only precise determination of pupil location but also a detailed analysis of the lens' optical properties. This process accurately determines which pixels' light rays successfully pass through the lens and reach the eye, where they are perceived. This analysis is crucial for understanding image formation and subsequent image processing. Light visibility analysis focuses on quantifying the amount of light entering the eye at each pixel. This step requires the use of a complex ray tracing model, which accurately tracks how light rays from each pixel, through refraction and reflection through the lens, ultimately reach the pupil. This quantitative analysis provides a deeper understanding of the brightness, contrast, and other characteristics of each image component, providing powerful support for subsequent image processing and analysis.

[0177] The specific analysis methods of S3 include but are not limited to:

[0178] (1) Geometric distortion analysis: By detecting the deformation of straight lines and geometric shapes in the sub-area, barrel distortion, pincushion distortion, etc. can be identified.

[0179] (2) Chromatic aberration analysis: Identify chromatic aberration problems caused by lens dispersion by detecting the shift of color in space.

[0180] Function: Distortion analysis is to accurately understand the distortion characteristics of each sub-region as the basis for subsequent distortion correction.

[0181] In step S4, based on the distortion analysis results, a distortion model is created for each sub-region, including a geometric distortion model and a chromatic aberration distortion model. The specific model creation method is as follows:

[0182] Geometric distortion model: Use a polynomial (such as a cubic polynomial) to fit the geometric distortion of the sub-region to obtain the distortion parameters.

[0183] Chromatic aberration distortion model: Use the chromatic aberration compensation method to fit the chromatic aberration distortion of the sub-region and obtain the chromatic aberration correction parameters.

[0184] Function: The distortion model is used in the design of subsequent correction algorithms and provides specific distortion parameters for precise correction.

[0185] In step S5, a correction algorithm is designed for each sub-region to correct its geometric and chromatic aberration distortion. The specific algorithm design is as follows:

[0186] Geometric distortion correction algorithm: Uses an inverse transformation method to correct barrel or pincushion distortion to a standard geometric shape. Common methods include using the distortion parameters to perform an inverse polynomial transformation.

[0187] Chromatic aberration distortion correction algorithm: Through color adjustment and gamma correction methods, it compensates for the color shift caused by chromatic aberration to ensure accurate color reproduction.

[0188] Function: The correction algorithm corrects the distortion of the sub-region and improves the image quality through specific mathematical transformation and color adjustment.

[0189] S6, applying the correction algorithm to each sub-region of the original display image to generate a corrected image. The specific steps are as follows:

[0190] A geometric distortion correction algorithm is applied to each sub-region to generate a geometrically corrected sub-region image.

[0191] A chromatic aberration distortion correction algorithm is applied to the geometrically corrected sub-region image to generate a final corrected sub-region image.

[0192] Purpose: The image correction step is the process of actually applying the correction algorithm to convert the original image into a corrected high-quality image.

[0193] In step S7, the corrected image is loaded back into the near-eye light field display device for display. Through the corrected image, the user can obtain a clearer and more realistic visual experience.

[0194] Function: Image update is the process of applying the correction results to the actual display device to verify the correction effect and improve the user experience.

[0195] like Figure 3 As shown, an embodiment of the present invention provides a region-based imaging correction system, specifically including:

[0196] Input module, used to obtain the original display image and preprocess it;

[0197] The region division and feature extraction module is used to divide the display image into multiple sub-regions and extract a set of feature points for each sub-region;

[0198] Distortion analysis and model creation module, used to perform geometric distortion and chromatic aberration analysis on each sub-region and create a model;

[0199] Correction module, used to design and implement geometric and chromatic aberration correction algorithms for each sub-region;

[0200] The output module is used to synthesize the corrected images of each sub-region into a final corrected image and output the corrected image.

[0201] 1. Specific application fields or related products of the present invention.

[0202] 1. Virtual reality (VR) and augmented reality (AR) display devices:

[0203] Near-Eye Displays: Improving the imaging quality of near-eye display devices, especially when using lens array technology, to solve the edge lens imaging problem.

[0204] Head-mounted displays (HMDs): Improve image clarity and field of view in HMD devices.

[0205] Light field display technology:

[0206] Light field display: Optimize pixel utilization efficiency in light field display and improve display clarity and resolution.

[0207] Holographic display: Improve imaging errors in holographic technology and enhance the realism of display effects.

[0208] 2. Medical imaging equipment:

[0209] Endoscopy: Improve the quality of endoscopic images, especially in marginal areas.

[0210] Microscopy: Improve image clarity and accuracy in microscopy applications, especially in multi-lens systems.

[0211] 3. Monitoring and security equipment:

[0212] High-resolution surveillance cameras: Improves the image quality of surveillance cameras at close range and in marginal areas.

[0213] Automotive camera systems: Improve edge imaging issues in automotive camera systems to enhance safety.

[0214] 4. Photography and videography:

[0215] Professional Cameras and Video Cameras: Enhances edge-to-edge image quality and overall image performance on high-end photographic and video equipment.

[0216] 5. Display panel and projection system:

[0217] Projectors: Improves image clarity and accuracy when using lens arrays in projectors.

[0218] Large-screen displays: Improves image distortion on large-screen displays and enhances display quality.

[0219] Related products

[0220] 1. Near-eye displays: such as VR headsets (e.g., Oculus Rift, HTC Vive, Meta Quest, etc.) and AR glasses (e.g., Microsoft HoloLens, Google Glass, etc.).

[0221] 2. Light field displays: such as the Lytro light field camera and light field display systems for scientific research.

[0222] 3. Medical imaging equipment: such as Olympus endoscopes, Zeiss microscopes, etc.

[0223] 4. Surveillance cameras: high-resolution surveillance cameras produced by companies such as Hikvision and Dahua.

[0224] 5. In-vehicle camera systems: such as driver assistance systems from Tesla and other automakers.

[0225] 6. Professional photographic equipment: high-end cameras and camcorders from brands such as Canon and Nikon.

[0226] 7. Projectors and display panels: such as high-resolution projectors from Epson and BenQ, and large-screen monitors from LG and Samsung.

[0227] These application areas and products can benefit significantly from technical solutions to enhance image quality and user experience.

[0228] 2. Relevant evidence of the technical effects obtained by the embodiments of the present invention.

[0229] The correction effect is verified through simulation and actual display equipment.

[0230] The correction experiment process is as follows:

[0231] Single lens imaging, steps:

[0232] (1) Each lens forms an image separately to obtain the original image.

[0233] (2) The imaging result of each lens will produce a large blank area around the image, and the pixel values in these areas are NaN (non-numeric).

[0234] 2. Create a Look-Up Table. Steps:

[0235] (1) Perform feature point detection on the imaging of each lens and extract the effective area.

[0236] (2) Filter out invalid areas, that is, blank areas (NaN data).

[0237] There are several methods for distortion correction:

[0238] 1. Geometric transformation: Use model-based methods such as polynomial models, fisheye models, and wide-angle lens models to correct distortion by calibrating parameters.

[0239] 2. Calibration method: Use a checkerboard or other known pattern to correct lens distortion by taking multiple shots and calculating calibration parameters.

[0240] 3. Image processing algorithm: Use image processing techniques such as back projection and image interpolation to correct geometric distortion in the image.

[0241] 4. Deep learning: Automatically detect and correct distortion by training a convolutional neural network (CNN).

[0242] Here is a detailed explanation:

[0243] 1. Geometric transformation method:

[0244] Polynomial model: Fits and corrects distortion through high-order polynomial functions.

[0245] Fisheye model: specially used to deal with fisheye lens distortion and correct the image through specific equations.

[0246] Wide-angle lens model: used to correct barrel distortion or pincushion distortion caused by wide-angle lenses.

[0247] 2. Calibration method:

[0248] Take multiple shots using a known pattern, such as a checkerboard.

[0249] By calculating the deformation of each pattern at different viewing angles, the distortion parameters of the lens are derived.

[0250] These parameters are used to correct the images taken subsequently.

[0251] 3. Image processing algorithm:

[0252] Back projection: Based on the known distortion parameters, the image is corrected by back projection.

[0253] Image interpolation: Rearrange and fill pixels in the deformed image through interpolation algorithm.

[0254] 4. Deep Learning Methods:

[0255] A convolutional neural network (CNN) is trained to identify and correct distortions.

[0256] The network learns how to convert distorted images into normal images through a large amount of training data.

[0257] These methods can be used alone or in combination to achieve better distortion correction effects.

[0258] 3. Image alignment, steps:

[0259] (1) Align the active area of each lens with the target image on the display.

[0260] (2) The alignment process includes two parts: size alignment and internal index data alignment: size alignment: adjusting the size and resolution of each lens image to make it consistent with the size of the corresponding area on the display screen; internal index data alignment: ensuring that the internal data index of each lens image is consistent with the image index on the display screen to achieve accurate pixel correspondence.

[0261] 4. Correction and calibration, steps:

[0262] (1) Based on the aligned image data, perform distortion correction and chromatic aberration correction using the previously created Look-UpTable.

[0263] (2) Ensure that the distortion of each lens imaging is effectively compensated to improve the clarity and visual quality of the overall image.

[0264] The above steps describe in detail how to handle the NaN data area during the correction experiment, ensure that the valid area is aligned with the image on the display, and ultimately achieve image distortion correction and quality improvement. After the above steps, if the present invention directly uses the Lookup Table (LUT) to transform the image, the image index will exceed the boundary, such as Figure 4 As shown, the left side is the original image, and the right side is the image after correction using LUT. The black area in the figure is the new invalid area, which represents the loss of resolution. In order to avoid the loss of resolution, the present invention needs to further optimize the LUT so that the display result fills all areas. The LUT index value is changed to index the global image. The global image is as follows Figure 5The overall imaging effect is shown as follows. Figure 6 The regional correction results are shown as follows. Figure 7 shown.

[0265] Figures 8-11 They are respectively the imaging results of the lens array taken by a high-definition camera, the display of the high-definition camera and experimental equipment, the separate display of the imaging effect of Lens 1, the results of image correction using different methods, and the display of the effects of the corresponding areas of the three lenses lens1-3.

[0266] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0267] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A region-based imaging correction method, characterized in that: The method includes: S1: Image acquisition and preprocessing: acquiring the original display image from the near-eye light field display device and preprocessing the original image; S2: Region division: Based on the structure of the lens array, the displayed image is divided into multiple sub-regions. Calculation is performed on the pixel area corresponding to each lens, which pixels have light entering the human eye through the corresponding lens, and which pixels have light not entering the human eye. The area of pixels that enter the human eye is then counted, and the image is corrected based on known feature points. The pixel areas that do not enter the human eye need to be filled using fitting or estimation methods. S3: Distortion analysis: Perform distortion analysis on each sub-region to identify the geometric and chromatic distortion characteristics of the region; S4: Distortion model creation: based on the distortion analysis results, a distortion model is created for each sub-region; S5: Correction algorithm design: design a correction algorithm suitable for each sub-region to correct its geometric and chromatic aberration distortion; S6: Image correction, applying the correction algorithm to each sub-region of the original display image to generate a corrected image; S7: Image update, loading the corrected image back into the near-eye light field display device for display.

2. The region-based imaging correction method according to claim 1, wherein: S1 obtains an original display image from a lens array near-eye light field display device. The image is generated by a lens array, and each lens is responsible for imaging a different sub-area. The image quality of these sub-areas varies due to different lens characteristics; the original image is preprocessed, including denoising and adjusting brightness and contrast.

3. The region-based imaging correction method according to claim 1, wherein: In S2, each sub-region corresponds to a lens, and the division method can be performed according to the geometric layout of the lens array, such as grid or honeycomb; a set of feature points is extracted from each sub-region as the basis for distortion analysis; The S2 specifically includes: (1) Identification of edge lens areas: First, identify the pixel areas corresponding to each lens, especially the edge lens areas; the light from many pixels in these areas cannot enter the pupil at the same time and therefore cannot be observed by the human eye; (2) Light visibility analysis: Calculate which pixels’ light enters the human eye through the corresponding lens. This can be determined by factors such as pupil position and lens optical properties; determine which areas of pixels are pixels that enter the human eye and which are pixels that do not enter the human eye; (3) Image correction: Correct the pixel areas that enter the human eye; based on known feature points, you can adjust the image in these areas to compensate for distortion or other image quality issues; (4) Filling and estimation: For pixel areas that do not enter the human eye, use fitting or estimation methods to fill these areas; these algorithms can generate reasonable image data based on the information of adjacent areas, thereby maintaining the consistency and integrity of the image.

4. The region-based imaging correction method according to claim 1, wherein: The specific analysis methods of S3 include but are not limited to: (1) Geometric distortion analysis: By detecting the deformation of straight lines and geometric shapes in the sub-area, barrel distortion and pincushion distortion can be identified; (2) Chromatic aberration analysis: Identify chromatic aberration problems caused by lens dispersion by detecting the shift of color in space.

5. The region-based imaging correction method according to claim 1, wherein: In S4, based on the distortion analysis results, a distortion model is created for each sub-region, including a geometric distortion model and a chromatic aberration distortion model. The specific model creation method is as follows: (1) Geometric distortion model: Use polynomials to fit the geometric distortion of the sub-region and obtain the distortion parameters; (2) Chromatic aberration distortion model: The chromatic aberration distortion of the sub-region is fitted using the chromatic aberration compensation method to obtain the chromatic aberration correction parameters.

6. The region-based imaging correction method according to claim 1, wherein: In step S5, a correction algorithm is designed for each sub-region to correct its geometric and chromatic aberration distortion. The specific algorithm design is as follows: (1) Geometric distortion correction algorithm: Use the inverse transformation method to correct the barrel or pincushion distortion to the standard geometric shape. Common methods include using the distortion parameters to perform inverse polynomial transformation; (2) Chromatic aberration distortion correction algorithm: Through color adjustment and gamma correction methods, the color shift caused by chromatic aberration is compensated to ensure accurate color reproduction.

7. The region-based imaging correction method according to claim 1, wherein: S6, applying the correction algorithm to each sub-region of the original display image to generate a corrected image, specifically the following steps: (1) Applying a geometric distortion correction algorithm to each sub-region to generate a geometrically corrected sub-region image; (2) Applying a chromatic aberration distortion correction algorithm to the geometrically corrected sub-region image to generate a final corrected sub-region image.

8. The region-based imaging correction method according to claim 1, wherein: Determine the pupil location; analyze the optical properties of the lens; and build a ray tracing model to track how light from each pixel passes through the lens and reaches the pupil location. Based on the results of ray tracing, determine which areas of pixels emit light that can enter the human eye; Quantify the amount of light entering the human eye at each visible pixel to provide in-depth analysis of image characteristics such as brightness and contrast; and perform subsequent image processing and analysis based on this quantitative analysis.

9. A region-based imaging correction system according to claim 1-8, characterized in that: The system specifically includes: An image acquisition and preprocessing module is used to acquire the original display image from the near-eye light field display device and perform preprocessing on the original image by denoising, adjusting brightness and contrast; A region division module is used to divide the displayed image into multiple sub-regions according to the structure of the lens array, and calculate the pixel area that enters the human eye in the pixel area corresponding to each lens; The distortion analysis module is used to perform geometric and chromatic aberration distortion analysis on each sub-region and identify the distortion characteristics of the region; A distortion model creation module is used to create a geometric distortion model and a chromatic aberration distortion model for each sub-region based on the distortion analysis results; Correction algorithm design module, used to design geometric distortion correction algorithm and chromatic aberration correction algorithm applicable to each sub-region; an image correction module, configured to apply a correction algorithm to each sub-region of the original displayed image to generate a corrected image; The image update module is used to load the corrected image back to the near-eye light field display device for display.

10. The region-based imaging correction system according to claim 8, wherein: The area division module specifically includes: an edge lens area identification unit, for identifying the pixel area corresponding to each lens, in particular the edge lens area; A light visibility analysis unit, used to calculate which pixels' light enters the human eye through the corresponding lens, and determine the pixel areas that enter the human eye and those that do not; An image correction unit, used to correct pixel areas entering the human eye and adjust the images of these areas based on known feature points; The filling and estimation unit is used to fill the pixel areas that are not visible to the human eye using fitting or estimation methods to maintain the consistency and integrity of the image; The distortion analysis module specifically includes: A geometric distortion analysis unit, configured to identify barrel distortion and pincushion distortion by detecting the deformation of straight lines and geometric shapes within a sub-region; The color difference analysis unit is used to identify the color difference problem caused by lens dispersion by detecting the color shift in space.

Images