A multi-viewing image fusion processing method and system for holographic projection

Abstract

The present invention relates to the field of picture fusion processing technology, specifically a multi-view picture fusion processing method and system for holographic projection, comprising the following steps: adjusting the focal length and projection angle of multiple projectors based on distance sensor and angle sensor data, automatically calculating and adjusting the picture ratio according to the audience's perspective and distance, matching the actual viewing conditions, and generating a picture adaptation result. In the present invention, the focal length and projection angle of the projector are automatically adjusted by integrating distance and angle sensing data to improve the visual adaptability and consistency of the holographic projection. The dynamic optimization mechanism adjusts the picture according to the specific position and perspective of the audience to ensure that the optimized visual experience adapts to the needs of each audience, reduce visual errors, adjust the color output of each projector, optimize the color dynamic range under the light distribution, enhance the performance of color balance and details, and accurately correct real-time three-dimensional modeling and geometric distortion to improve the overall accuracy and quality of the holographic image.

Description

Technical Field

[0001] The present invention relates to the technical field of image fusion processing, and in particular to a multi-viewing angle image fusion processing method and system for holographic projection. Background Art

[0002] Image fusion processing technology mainly involves integrating image data from multiple perspectives into a coherent picture, and is widely used in video production, virtual reality, augmented reality, and holographic projection. Through advanced algorithms, multi-view image fusion technology can generate high-quality three-dimensional visual effects, allowing viewers to observe stereoscopic images of objects from multiple angles, including image alignment, color and lighting adjustment, and depth perception enhancement, etc., to optimize visual output and reduce visual distortion.

[0003] Among them, the multi-view image fusion processing method for holographic projection is a method for image processing for holographic projection, which can integrate multiple projection sources at different angles. Its main purpose is to improve the image quality and viewing experience in holographic projection technology. Through multi-view fusion, a more realistic three-dimensional effect can be achieved, enhancing the audience's sense of immersion, which is particularly valuable in exhibitions, education, entertainment, and advanced communication systems, especially when complex details need to be displayed or remote collaboration is required.

[0004] Although the existing multi-view image fusion technology is widely used in many fields, in holographic projection applications, the fixed projection setting lacks the ability to dynamically adapt to the audience's position and perspective, which may cause image ratio mismatch and visual distortion when the audience moves to watch, affecting the viewing experience and image authenticity. In addition, the existing technology usually adopts a unified processing for color adjustment, which fails to effectively solve the color unevenness problem caused by the difference in projection environment lighting, which is particularly obvious in occasions with strict requirements for high-quality visual output. These shortcomings restrict the application effect of the technology, especially in occasions that pursue high visual quality and user experience. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a multi-viewing image fusion processing method and system for holographic projection.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a multi-view image fusion processing method for holographic projection, comprising the following steps:

[0007] S1: Based on the distance sensor and angle sensor data, adjust the focal length and projection angle of multiple projectors, automatically calculate and adjust the picture ratio according to the audience's viewing angle and distance, match the actual viewing conditions, and generate picture adaptation results;

[0008] S2: According to the picture adaptation result, the color output configuration of each projector is adjusted one by one, the color depth and details of each picture are enhanced separately, the light distribution in the picture is analyzed, and the color dynamic range is adjusted according to the analysis result to match the visual effects of the dark and bright parts, the color balance and detail performance of the different projected pictures are unified, and the color enhancement result is generated;

[0009] S3: According to the color enhancement result, monitor the real-time audience distribution, dynamically adjust the contrast of each picture, match the actual position of the audience in the projection area, dynamically optimize the picture according to the audience's perspective, adjust the picture output in real time, and generate a dynamic visual adjustment result;

[0010] S4: Based on the dynamic visual adjustment results, the projection area is modeled in real time in three dimensions to identify the geometric distortion caused by changes in projection angle and distance. The intersection and overlapping parts of the projection surfaces are analyzed according to the recognition results, and deep geometric calibration is performed to ensure the consistency of the geometric image and obtain the multi-projector image fusion processing results.

[0011] As a further solution of the present invention, the step of obtaining the picture adaptation result is:

[0012] S111: collecting data from the distance sensor and the angle sensor, obtaining the accurate distance and viewing angle between the audience and the screen, and generating initial distance and angle data;

[0013] S112: Utilizing the initial distance and angle data, optimizing the projection angle of the projector and adjusting the focal length, using the formula:

[0014] Calculate and output the new focal length value ,in, represents the original focal length, is the adjusted distance, D is the initial audience distance;

[0015] S113: According to the new focal length value, the screen ratio of the projector is adjusted to match the actual viewing conditions of the current audience, and a screen adaptation result is generated.

[0016] As a further solution of the present invention, the analysis step of the illumination distribution in the picture is:

[0017] S211: According to the picture adaptation result, the current color output saturation of each projector is collected and recorded as initial color configuration data;

[0018] S212: Based on the initial color configuration data, the color depth and details are adjusted in combination with the target requirements of each picture, using the formula:

[0019] Perform color optimization and calculate the new color saturation after adjustment , generate the adjusted color configuration data, where is the initial color saturation, is the target color saturation, It is a coefficient adjusted according to the content of the picture;

[0020] S213: Analyze the lighting conditions of each picture according to the adjusted color configuration data, including lighting intensity and distribution measurement, and generate a lighting distribution analysis record.

[0021] As a further solution of the present invention, the step of obtaining the color enhancement result is:

[0022] S221: According to the illumination distribution analysis record, the illumination distribution characteristics of each picture are evaluated, focusing on the contrast between the dark part and the bright part, using the formula:

[0023]

[0024] Calculate the adjusted brightness dynamic range value , generating adjusted color dynamic range data, where is the current brightness dynamic range value, is the target brightness dynamic range value, is the adjustment factor;

[0025] S222: According to the adjusted color dynamic range data, refining the color balance adjustment of each projection picture, unifying the color and detail performance of the different projection pictures, and generating unified color balance data;

[0026] S223: Apply the unified color balance data to optimize the final visual output of each projector image, ensure the consistency of the overall display effect, and generate a color enhancement result.

[0027] As a further solution of the present invention, the step of obtaining the dynamic visual adjustment result is:

[0028] S311: Based on the color enhancement result, deploy cameras to capture the distribution of audiences in the projection area in real time, and generate real-time audience distribution data;

[0029] S312: Using the real-time audience distribution data, dynamically adjust the contrast of each picture using the formula:

[0030] Calculate the new contrast , generates an adjusted contrast setting where is the initial contrast, is the average distance of the current audience, is the optimal viewing distance. It is an adjustment factor determined based on the sensitivity of the audience position change;

[0031] S313: According to the adjusted contrast setting, the visual output of each picture is optimized to match the actual viewing angle and position of the audience, ensure the optimal visual experience, and generate a dynamic visual adjustment result.

[0032] As a further solution of the present invention, the step of identifying the geometric distortion is:

[0033] S411: Based on the dynamic visual adjustment result, construct a three-dimensional model of the projection area in real time, and obtain a three-dimensional model result by accurately capturing spatial coordinates and constructing facets;

[0034] S412: extracting key data points that affect the projection quality according to the three-dimensional model result, analyzing the geometric distortion of the key data points caused by changes in projection angle and distance, calculating the deviation of each data point from its ideal position, and obtaining a preliminary deviation calculation result;

[0035] S413: Perform in-depth analysis on the preliminary deviation calculation result, using the formula:

[0036] Calculate the distortion population standard deviation , and obtain the geometric distortion recognition result, where It is The actual deviation of the data points, is the average of the deviations of all data points, is the total number of data points.

[0037] As a further solution of the present invention, the step of obtaining the multi-projector image fusion processing result is:

[0038] S421: Analyze the intersection and overlap of the projection surfaces according to the geometric distortion recognition result, and obtain the intersection area analysis result by mapping the data points of the intersection area of ​​the projection surfaces;

[0039] S422: Based on the intersection area analysis result, perform depth geometry calibration, adjust the alignment accuracy of data points in the overlapping area one by one, optimize the visual error between the projection surfaces, and obtain a depth calibration result;

[0040] S423: According to the depth calibration result, using the formula:

[0041] Calculate the uniformity measure across the projection surface , and obtain the multi-projector image fusion processing result, where and It is The coordinates of the calibration points, and is the average value of all calibration point coordinates, is the total number of calibration points.

[0042] A multi-view image fusion processing system for holographic projection, comprising:

[0043] The distance sensing optimization module collects distance sensor and angle sensor data, analyzes the exact distance and viewing angle between the audience and the screen, optimizes the projection angle of the projector, and adjusts the focal length and the projector's screen ratio to match the actual viewing conditions and generate screen adaptation results;

[0044] The color output analysis module collects the current color output saturation of each projector according to the picture adaptation result, adjusts the color depth and details in combination with the target requirements of each picture, analyzes the lighting conditions of each picture, and generates a lighting distribution analysis record;

[0045] The color dynamic adjustment module evaluates the illumination distribution characteristics of each picture according to the illumination distribution analysis record, calculates the adjusted brightness dynamic range value, refines the color balance adjustment of each projected picture, and generates a color enhancement result;

[0046] The visual output optimization module captures the distribution of viewers in the projection area in real time based on the color enhancement result, dynamically adjusts the contrast of each picture, optimizes the visual output of each picture, matches the actual viewing angle and position of the audience, and generates a dynamic visual adjustment result;

[0047] The 3D modeling and analysis module constructs a 3D model of the projection area in real time based on the dynamic visual adjustment result, extracts key data points that affect the projection quality, analyzes the geometric distortion of the key data points caused by changes in projection angle and distance, calculates the overall standard deviation of the distortion, and obtains the geometric distortion recognition result;

[0048] The geometric calibration integration module analyzes the intersection and overlapping parts of the projection surface according to the geometric distortion recognition result, performs deep geometric calibration, adjusts the alignment accuracy of data points in the overlapping area one by one, calculates the uniformity measurement value of the entire projection surface, and obtains the multi-projector image fusion processing result.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are:

[0050] In the present invention, the focal length and projection angle of the projector are automatically adjusted by integrating distance and angle sensing data, thereby improving the visual adaptability and consistency of the holographic projection. The dynamic optimization mechanism adjusts the picture according to the specific position and viewing angle of the audience to ensure that the optimized visual experience adapts to the needs of each audience, thereby reducing visual errors, independently adjusting the color output of each projector and optimizing the color dynamic range under the light distribution, greatly enhancing the color balance and detail performance, improving the realism of the three-dimensional effect, real-time three-dimensional modeling and precise correction of geometric distortion, and improving the overall accuracy and quality of the holographic image. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the steps of the present invention;

[0052] Figure 2 A flowchart of obtaining the screen adaptation result of the present invention;

[0053] Figure 3 The figure is a flow chart of analyzing the light distribution in the picture of the present invention;

[0054] Figure 4 A flowchart of obtaining color enhancement results of the present invention;

[0055] Figure 5 A flowchart of obtaining dynamic visual adjustment results of the present invention;

[0056] Figure 6 This is a flow chart of the identification of geometric distortion of the present invention;

[0057] Figure 7 The present invention is a flowchart for obtaining the multi-projector image fusion processing results. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and 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.

[0059] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0060] See also Figure 1 , a multi-view image fusion processing method for holographic projection, comprising the following steps:

[0061] S1: Based on the distance sensor and angle sensor data, adjust the focal length and projection angle of multiple projectors, automatically calculate and adjust the picture ratio according to the audience's viewing angle and distance, match the actual viewing conditions, and generate picture adaptation results;

[0062] S2: According to the picture adaptation results, adjust the color output configuration of each projector one by one, enhance the color depth and details of each picture separately, analyze the light distribution in the picture, adjust the color dynamic range according to the analysis results to match the visual effects of dark and bright parts, unify the color balance and detail performance of different projected pictures, and generate color enhancement results;

[0063] S3: Based on the color enhancement results, monitor the real-time audience distribution, dynamically adjust the contrast of each picture, match the actual position of the audience in the projection area, dynamically optimize the picture according to the audience's perspective, adjust the picture output in real time, and generate dynamic visual adjustment results;

[0064] S4: Based on the dynamic visual adjustment results, the projection area is modeled in real time in 3D to identify the geometric distortion caused by changes in projection angle and distance. The intersection and overlapping parts of the projection surface are analyzed according to the recognition results, and deep geometric calibration is performed to ensure the consistency of the geometric image and obtain the multi-projector image fusion processing results.

[0065] The picture adaptation results include focus state adjustment results, angle setting results and viewing ratio adjustment records; the color enhancement results include color saturation adjustment results, detail clarity adjustment results and dynamic range analysis results; the dynamic vision adjustment results include contrast adjustment records, viewing angle matching results and real-time output optimization records; the multi-projector picture fusion processing results include three-dimensional model analysis results, geometric correction results and picture consistency analysis records.

[0066] See also Figure 2 , the steps to obtain the screen adaptation results are:

[0067] S111: collecting data from the distance sensor and the angle sensor, obtaining the accurate distance and viewing angle between the audience and the screen, and generating initial distance and angle data;

[0068] S112: Utilize the initial distance and angle data to optimize the projection angle of the projector and adjust the focal length using the formula:

[0069] Calculate and output the new focal length value ,in, represents the original focal length, is the adjusted distance, D is the initial audience distance;

[0070] S113: According to the new focal length value, the screen ratio of the projector is adjusted to match the actual viewing conditions of the current audience, and a screen adaptation result is generated.

[0071] Collect and analyze data from the distance sensor and angle sensor, using the formula,

[0072] Convert to get more accurate distance measurement (adjusted distance), where D represents the actual distance between the audience and the screen obtained directly from the distance sensor (initial audience distance), represents the viewing angle obtained from the angle sensor, and H represents the height from the screen to the ground.

[0073] Get the audience distance D is 5 meters, the screen height H is 2 meters, and the viewing angle is 30°.

[0074] Calculate the total distance:

[0075]

[0076] The results show that after taking the viewing angle into account, the actual visual distance between the audience and the screen increased slightly (from 5 meters to 5.13 meters).

[0077] Calculate the new focal length using the formula , collect the original focal length is 1000 mm, the initial audience distance is 5 meters, and the adjusted distance is 5.13 meters, then:

[0078]

[0079] The results showed that due to the increased audience distance, the focal length of the projector needed to be increased (from 1000mm to 1026mm) to maintain image clarity.

[0080] In the process of adjusting the projector screen ratio, the formula Calculate new aspect ratio , adapting to the audience's perspective and distance changes. In the formula, Represents the original aspect ratio. represents the newly calculated focal length, Represents the original focal length.

[0081] Original aspect ratio 16:9, original focal length is 1000 mm, the new focal length calculated from the previous step is The width is 1026 mm. Substitute it into the formula to calculate the new aspect ratio:

[0082]

[0083] It turns out that in order to maintain image quality and accommodate new viewer distances and focus adjustments, the aspect ratio needs to increase accordingly.

[0084] See also Figure 3 , the analysis steps of the light distribution in the picture are:

[0085] S211: According to the picture adaptation result, the current color output saturation of each projector is collected and recorded as initial color configuration data;

[0086] S212: Based on the initial color configuration data, combined with the target requirements of each picture, adjust the color depth and details, using the formula,

[0087] Perform color optimization and calculate the new color saturation after adjustment , generate the adjusted color configuration data, where is the initial color saturation, is the target color saturation, It is a coefficient adjusted according to the picture content;

[0088] S213: Analyze the lighting conditions of each picture according to the adjusted color configuration data, including lighting intensity and distribution measurement, and generate a lighting distribution analysis record.

[0089] Collect the current color output color saturation of each projector, perform standardization, and use the formula , generate standardized color configuration data. In the formula, is the standardized value, Represent the original color saturation, and Represent the theoretical minimum and maximum values ​​respectively.

[0090] Color Saturation Measured as 200, theoretical minimum 0 and the maximum value is 255, then the normalized value of saturation will be calculated as:

[0091]

[0092] This means that the normalized saturation value is 0.7843, indicating that the saturation setting of this projector is 78.43% within the allowed range.

[0093] Color saturation obtained by normalization The target color saturation is 0.7843. The target color saturation is determined through user feedback and content analysis. is 0.85. Set the adjustment factor A value of 0.5 means that the desired adjustment is 50% closer to the target value.

[0094] Substitute into the formula to calculate:

[0095]

[0096] The calculation results show that, considering the current color configuration and the target configuration, the adjusted new color saturation is approximately 0.81715, ensuring that the color output is closer to the needs of users and content.

[0097] Continue to analyze the lighting conditions of each frame, including measurements of light intensity and distribution. Apply the formula The lighting of each frame is adjusted to match the adjusted color configuration. Represents the adjusted light intensity, Represents the currently measured light intensity, It is an adjustment factor that is dynamically determined based on the picture content and audience feedback.

[0098] Collect the current light intensity of a certain screen Measured at 500Ix, adjustment factor determined based on image analysis The light intensity after adjustment is 0.8 to reduce the visual discomfort caused by the overly bright picture. :

[0099]

[0100] The results show that by reducing the light intensity from 500Ix to 400Ix to adapt to specific visual environments and content display requirements, ensuring that the lighting and color settings of the picture are coordinated, the overall visual quality can be improved.

[0101] See also Figure 4 , the steps to obtain the color enhancement results are:

[0102] S221: Based on the illumination distribution analysis record, evaluate the illumination distribution characteristics of each picture, pay attention to the contrast between dark and bright parts, and use the formula:

[0103]

[0104] Calculate the adjusted brightness dynamic range value , generating adjusted color dynamic range data, where is the current brightness dynamic range value, is the target brightness dynamic range value, K is the adjustment coefficient;

[0105] S222: According to the adjusted color dynamic range data, the color balance adjustment of each projection picture is refined, the color and detail performance of the different projection pictures are unified, and unified color balance data is generated;

[0106] S223: Apply unified color balance data to optimize the final visual output of each projector image, ensure the consistency of the overall display effect, and generate color enhancement results.

[0107] Get the dynamic range value of the current picture from the light distribution analysis record The target dynamic range is 2.5 (indicating the brightness range of the current picture, usually based on the logarithmic ratio of the light intensity), and the target dynamic range value is determined through visual and content analysis. It should be 3.0. The adjustment coefficient K is set to 0.2, indicating that the desired adjustment range is 20% of the difference between the target and the current value. Substituting it into the formula, we get:

[0108]

[0109] The results show that the dynamic range is increased from 2.5 to 2.75, making the dynamic range of the picture closer to the target dynamic range of 3.0, which helps to improve the detail display of the picture, especially in the dark and bright areas, enhances the overall visual experience, and ensures that the audience can get a richer and more dynamic visual experience.

[0110] Continue to refine the color balance using the dynamic range adjustment data to ensure that there is no perceptible difference in color between all projected images, applying the formula Make adjustments, including Represents the adjusted color data (the adjusted new color saturation), is the color balance target value determined based on user feedback and content optimization. Based on the lighting adjustment result Dynamically determined adjustment factors are used to refine the color balance of each picture.

[0111] Combined with the above calculation results for, is 0.817, and the target color is balanced Set the adjustment factor to 0.9 A value of 0.5 means that you want to make a moderate adjustment to the color balance.

[0112] Perform specific calculations:

[0113]

[0114] The calculation results show that after the color balance algorithm is processed, the color setting is adjusted from 0.817 to about 0.851, which is closer to the target color balance value of 0.9. This adjustment helps to ensure the consistency of color of all projected images, making the viewing experience seamless between different images, thereby improving the overall visual effect.

[0115] See also Figure 5 , the steps to obtain the dynamic visual adjustment results are:

[0116] S311: Based on the color enhancement result, deploy cameras to capture the distribution of audiences in the projection area in real time and generate real-time audience distribution data;

[0117] S312: Using real-time audience distribution data, dynamically adjust the contrast of each picture using the formula,

[0118] Calculate the new contrast , generates an adjusted contrast setting where is the initial contrast, is the average distance of the current audience, is the optimal viewing distance. It is an adjustment factor determined based on the sensitivity of the audience position change;

[0119] S313: According to the adjusted contrast setting, the visual output of each picture is optimized to match the actual viewing angle and position of the audience, ensure the optimal visual experience, and generate a dynamic visual adjustment result.

[0120] Collect initial contrast 1.0, ideal viewing distance Set to 10 meters, and get the average distance of the current audience from the real-time audience distribution data For 12 meters, set the adjustment coefficient It is -0.1, reflecting that the contrast should gradually decrease as the audience distance increases (because when the distance is far, too high contrast may cause visual discomfort).

[0121] The specific calculation process is as follows:

[0122]

[0123] Calculated by the formula, the adjusted contrast It drops to about 0.819, which is slightly lower than the initial contrast. This adjustment helps ensure a more comfortable viewing experience for viewers at a distance, indicating that the adjusted contrast is more suitable for long-distance viewing than the original setting. The appropriate reduction in contrast helps alleviate visual fatigue that viewers may experience due to excessively high contrast, thereby optimizing the overall viewing experience. The dynamic adjustment mechanism based on the actual distribution of the audience ensures that the picture output can flexibly adapt to the actual needs of the audience, thereby improving the universality of the display effect and the comfort of viewing.

[0124] See also Figure 6 , the identification steps of geometric distortion are:

[0125] S411: Based on the dynamic visual adjustment result, a three-dimensional model of the projection area is constructed in real time, and a three-dimensional model result is obtained through accurate spatial coordinate capture and facet construction;

[0126] S412: extracting key data points that affect the projection quality based on the three-dimensional model results, analyzing the geometric distortion of the key data points caused by changes in projection angle and distance, calculating the deviation of each data point from its ideal position, and obtaining a preliminary deviation calculation result;

[0127] S413: Conduct in-depth analysis of the preliminary deviation calculation results, using the formula,

[0128] Calculate the distortion population standard deviation , and obtain the geometric distortion recognition result, where It is The actual deviation of the data points, is the average of the deviations of all data points, is the total number of data points.

[0129] Extract key data points from the 3D model results, according to the formula,

[0130] Calculate the deviation metric for each data point In the formula, represents the average deviation metric of the data points, Representative The actual deviation of the data points, represents the average deviation of all data points, and n represents the total number of data points.

[0131] Three data points were collected with deviations of 2, -2, and 5 (in millimeters).

[0132] Calculate the mean deviation:

[0133]

[0134] Compute the absolute difference of each deviation from the mean deviation:

[0135]

[0136] Compute the mean deviation metric:

[0137]

[0138] The results show that the average deviation metric is 2.44 mm, which reflects the average degree of geometric deviation of the data points in the 3D model, and directly affects the accuracy and effect of geometric correction in subsequent steps.

[0139] The overall standard deviation of the deviations of the 3D model data points is then calculated to measure the overall geometric distortion of the data points.

[0140] The deviation data obtained using the previous steps are: 2, -2 and 5 mm, =1.67 mm.

[0141] Compute the squared deviation of each deviation value from the mean deviation:

[0142]

[0143] Compute the sum of squared differences:

[0144]

[0145] Dividing the sum of the squared differences by the total number of data points n=3 gives:

[0146]

[0147] Calculate the standard deviation:

[0148]

[0149] The results show that the overall standard deviation of the average deviation is 2.87 mm, which reflects the statistical dispersion of the overall data point deviation, which is higher than the simple average deviation metric and provides a more comprehensive geometric distortion assessment. This value will be used to evaluate and guide subsequent geometric correction operations to ensure the geometric consistency of the projected image.

[0150] See also Figure 7 , the steps to obtain the multi-projector image fusion processing results are:

[0151] S421: Analyze the intersection and overlap of the projection surfaces according to the geometric distortion recognition result, and obtain the intersection area analysis result by mapping the data points of the intersection area of ​​the projection surfaces;

[0152] S422: Based on the intersection area analysis result, perform depth geometry calibration, adjust the alignment accuracy of data points in the overlapping area one by one, optimize the visual error between the projection surfaces, and obtain the depth calibration result;

[0153] S423: According to the depth calibration result, the formula is used:

[0154]

[0155] Calculate the uniformity measurement value U of the entire projection surface and obtain the multi-projector image fusion processing result, where: and It is The coordinates of the calibration points, and is the average value of all calibration point coordinates, is the total number of calibration points.

[0156] Collect the coordinates of the three calibration points as follows:

[0157]

[0158] Compute the mean for each coordinate:

[0159]

[0160] Compute the distance from each point to the mean point:

[0161] For point 1:

[0162]

[0163] For point 2:

[0164]

[0165] For point 3:

[0166]

[0167] Calculate the value of U:

[0168]

[0169] The results show that the uniformity metric of the projection surface is 2.07, which means that the average distance between each calibration point and the average position is 2.07 units. This value reflects the dispersion of the overall calibration points. A smaller value means higher image consistency, thus ensuring the high quality of the multi-projector image fusion processing results.

[0170] A multi-view image fusion processing system for holographic projection, comprising:

[0171] The distance sensing optimization module collects distance sensor and angle sensor data, analyzes the exact distance and viewing angle between the audience and the screen, optimizes the projection angle of the projector, and adjusts the focal length and the projector's screen ratio to match the actual viewing conditions and generate screen adaptation results;

[0172] The color output analysis module collects the current color output saturation of each projector according to the picture adaptation results, adjusts the color depth and details according to the target requirements of each picture, analyzes the lighting conditions of each picture, and generates a light distribution analysis record;

[0173] The color dynamic adjustment module evaluates the light distribution characteristics of each picture based on the light distribution analysis records, calculates the adjusted brightness dynamic range value, refines the color balance adjustment of each projected picture, and generates color enhancement results;

[0174] Based on the color enhancement results, the visual output optimization module captures the distribution of the audience in the projection area in real time, dynamically adjusts the contrast of each picture, optimizes the visual output of each picture, matches the actual viewing angle and position of the audience, and generates dynamic visual adjustment results;

[0175] The 3D modeling and analysis module builds a 3D model of the projection area in real time based on the dynamic visual adjustment results, extracts key data points that affect the projection quality, analyzes the geometric distortion of key data points caused by changes in projection angle and distance, calculates the overall standard deviation of the distortion, and obtains the geometric distortion recognition results;

[0176] The geometric calibration integration module analyzes the intersection and overlapping parts of the projection surface according to the results of geometric distortion recognition, performs deep geometric calibration, adjusts the alignment accuracy of data points in the overlapping area one by one, calculates the uniformity measurement value of the entire projection surface, and obtains the multi-projector image fusion processing result.

[0177] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A multi-view image fusion processing method for holographic projection, characterized in that: The following steps are involved: Based on the distance sensor and angle sensor data, the focal length and projection angle of multiple projectors are adjusted, and the picture ratio is automatically calculated and adjusted according to the audience's viewing angle and distance to match the actual viewing conditions and generate picture adaptation results; According to the picture adaptation result, the color output configuration of each projector is adjusted one by one, the color depth and details of each picture are enhanced separately, the light distribution in the picture is analyzed, and the color dynamic range is adjusted according to the analysis result to match the visual effects of the dark and bright parts, the color balance and detail performance of the different projected pictures are unified, and the color enhancement result is generated; According to the color enhancement result, monitor the real-time audience distribution, dynamically adjust the contrast of each picture, match the actual position of the audience in the projection area, dynamically optimize the picture according to the audience's perspective, adjust the picture output in real time, and generate dynamic visual adjustment results; Based on the dynamic visual adjustment result, the projection area is modeled in real time in three dimensions to identify the geometric distortion caused by the change of projection angle and distance. According to the identification result, the intersection and overlapping parts of the projection surface are analyzed to perform deep geometric calibration to ensure the consistency of the geometric picture and obtain the multi-projector picture fusion processing result. The steps for obtaining the dynamic visual adjustment result are: Based on the color enhancement result, deploying cameras to capture the distribution of audiences in the projection area in real time, and generating real-time audience distribution data; Using the real-time audience distribution data, the contrast of each picture is dynamically adjusted using the formula: Calculate the new contrast , generates an adjusted contrast setting where is the initial contrast, is the average distance of the current audience, is the optimal viewing distance. It is an adjustment factor determined based on the sensitivity of the audience position change; According to the adjusted contrast setting, the visual output of each picture is optimized to match the actual viewing angle and position of the audience, ensure the best visual experience, and generate a dynamic visual adjustment result; The steps of identifying the geometric distortion are: Based on the dynamic visual adjustment result, a three-dimensional model of the projection area is constructed in real time, and a three-dimensional model result is obtained by accurately capturing spatial coordinates and constructing facets; Extract key data points that affect projection quality based on the three-dimensional model results, analyze geometric distortion of key data points caused by changes in projection angle and distance, calculate the deviation of each data point from its ideal position, and obtain preliminary deviation calculation results; The preliminary deviation calculation results are analyzed in depth, using the formula, Calculate the distortion population standard deviation , and obtain the geometric distortion recognition result, where It is The actual deviation of the data points, is the average of the deviations of all data points, is the total number of data points; The steps for obtaining the multi-projector image fusion processing result are: According to the geometric distortion recognition result, the intersection and overlap of the projection surfaces are analyzed, and the intersection area analysis result is obtained by mapping the data points of the intersection area of ​​the projection surfaces; Based on the intersection area analysis result, a depth geometry calibration is performed, the alignment accuracy of the data points in the overlapping area is adjusted one by one, the visual error between the projection surfaces is optimized, and a depth calibration result is obtained; According to the depth calibration result, the formula is used, Calculate the uniformity measure across the projection surface , and obtain the multi-projector image fusion processing result, where and It is The coordinates of the calibration points, and is the average value of all calibration point coordinates, is the total number of calibration points.

2. The multi-view image fusion processing method for holographic projection according to claim 1, characterized in that: The steps for obtaining the picture adaptation result are: Collect data from the distance sensor and angle sensor to obtain the accurate distance and viewing angle between the audience and the screen, and generate initial distance and angle data; Using the initial distance and angle data, the projection angle of the projector is optimized and the focal length is adjusted, using the formula: Calculate and output the new focal length value ,in, represents the original focal length, To adjust the distance, is the initial audience distance; According to the new focal length value, the screen ratio of the projector is adjusted to match the actual viewing conditions of the current audience, and a screen adaptation result is generated.

3. The multi-view image fusion processing method for holographic projection according to claim 2, characterized in that: The analysis steps of the illumination distribution in the picture are as follows: According to the picture adaptation result, the current color output saturation of each projector is collected and recorded as initial color configuration data; Based on the initial color configuration data, combined with the target requirements of each picture, adjust the color depth and details, and use the formula, Perform color optimization and calculate the new color saturation after adjustment , generate the adjusted color configuration data, where is the initial color saturation, is the target color saturation, It is a coefficient adjusted according to the content of the picture; According to the adjusted color configuration data, the lighting conditions of each picture are analyzed, including the measurement of the lighting intensity and distribution, and a lighting distribution analysis record is generated.

4. The multi-view image fusion processing method for holographic projection according to claim 3, characterized in that: The steps for obtaining the color enhancement result are: According to the illumination distribution analysis record, the illumination distribution characteristics of each picture are evaluated, focusing on the contrast between dark and bright parts, using the formula: Calculate the adjusted brightness dynamic range value , generating adjusted color dynamic range data, where is the current brightness dynamic range value, is the target brightness dynamic range value, is the adjustment factor; According to the adjusted color dynamic range data, the color balance adjustment of each projection picture is refined, the color and detail performance of the different projection pictures are unified, and unified color balance data is generated; The unified color balance data is applied to optimize the final visual output of each projector image, ensure the consistency of the overall display effect, and generate a color enhancement result.

5. A multi-view image fusion processing system for holographic projection, characterized in that: The system is used to execute the multi-view image fusion processing method for holographic projection according to any one of claims 1 to 4, and comprises: The distance sensing optimization module collects distance sensor and angle sensor data, analyzes the exact distance and viewing angle between the audience and the screen, optimizes the projection angle of the projector, and adjusts the focal length and the projector's screen ratio to match the actual viewing conditions and generate screen adaptation results; The color output analysis module collects the current color output saturation of each projector according to the picture adaptation result, adjusts the color depth and details in combination with the target requirements of each picture, analyzes the lighting conditions of each picture, and generates a lighting distribution analysis record; The color dynamic adjustment module evaluates the illumination distribution characteristics of each picture according to the illumination distribution analysis record, calculates the adjusted brightness dynamic range value, refines the color balance adjustment of each projected picture, and generates a color enhancement result; The visual output optimization module captures the distribution of viewers in the projection area in real time based on the color enhancement result, dynamically adjusts the contrast of each picture, optimizes the visual output of each picture, matches the actual viewing angle and position of the audience, and generates a dynamic visual adjustment result; The 3D modeling and analysis module constructs a 3D model of the projection area in real time based on the dynamic visual adjustment result, extracts key data points that affect the projection quality, analyzes the geometric distortion of the key data points caused by changes in projection angle and distance, calculates the overall standard deviation of the distortion, and obtains the geometric distortion recognition result; The geometric calibration integration module analyzes the intersection and overlapping parts of the projection surface according to the geometric distortion recognition result, performs deep geometric calibration, adjusts the alignment accuracy of data points in the overlapping area one by one, calculates the uniformity measurement value of the entire projection surface, and obtains the multi-projector image fusion processing result.

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