Method for correcting side projection blur of projector

Abstract

The invention relates to the technical field of projection display, and discloses a projector side projection blur correction method comprising the following steps: S1, data acquisition; s2, calculating a side projection angle adjustment value; s3, adjusting the angle of the projector; s4, establishing a model; s5, calculating a fuzzy kernel; s6, restoring the image; and S7, outputting a corrected image, through accurate data acquisition, angle adjustment, model establishment and image restoration steps, the definition of a projected image during side projection is significantly improved, the adjustment process is more objective and accurate, the image quality of a final correction result is ensured, and through a Gaussian blur model, the image quality is improved. Compared with the prior art, other types of fuzzy models are considered for comparison and selection, the simulation and recovery capability of the model to image blurring is improved, a deep learning method is combined for training the blurring kernel, the calculation precision and adaptability of the blurring kernel are improved, and a more accurate basis is provided for image recovery.

Description

Technical Field

[0001] The invention relates to the technical field of projection display, and in particular to a method for correcting blur of side projection of a projector. Background Art

[0002] A projector, also known as a projector, is a device that can project images or videos onto a screen. It can be connected to computers, VCDs, DVDs, BDs, game consoles, DVs, etc. through different interfaces to play corresponding video signals. As an important optical device, a projector can provide consumers with rich and exciting content, and is favored by young people because of its compact and lightweight features. Although the existing projectors have performed perfectly in terms of front projection, there are still certain limitations in terms of side projection. For example, due to the optical characteristics of the projector and the limitations of the display chip, the depth of field cannot be made very large, which leads to the phenomenon that part of the picture is clear and part is blurred when projecting sideways. This phenomenon will deepen as the side projection angle increases, thereby affecting the user's viewing experience.

[0003] As a common display device, projectors are widely used in education, conferences, entertainment and other fields. However, in actual use, when the projector is projected in a side projection mode, the image is often blurred due to factors such as projection angle and light refraction, affecting the audience's viewing experience.

[0004] When the projector is projected sideways, there is a certain angle between the projection lens and the projection screen, which often causes trapezoidal distortion and blurring of the projected image. Traditional correction methods often rely on manual adjustment of the angle and focal length of the projector, which is not only cumbersome to operate, but also difficult to ensure the correction effect. Therefore, a correction method for the blur of projector side projection is proposed. Summary of the invention

[0005] The object of the present invention is to provide a method for correcting the side projection blur of a projector to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for correcting side projection blur of a projector, comprising the following steps:

[0007] S1, data collection;

[0008] S2, calculating the side projection angle adjustment value;

[0009] S3, adjust the projector angle;

[0010] S4, build model;

[0011] S5, fuzzy kernel calculation;

[0012] S6, image restoration;

[0013] S7. Output the corrected image.

[0014] Preferably, in S1, the following sub-steps are included:

[0015] S101, capturing multiple images using the same test pattern at different side projection angles;

[0016] S102, scoring the clarity of each image;

[0017] S103, recording each side projection angle and the corresponding image clarity score.

[0018] Preferably, in S2, the calculation formula for adjusting the side projection angle is:

[0019]

[0020] Where H is the height of the projected image, D is the distance between the projector and the projection screen, and α is the initial vertical angle.

[0021] Preferably, in S3, the angle of the projector lens is adjusted according to the calculation formula result of the angle adjustment so that the new vertical angle is α+Δα.

[0022] Preferably, in S4, the following sub-steps are included:

[0023] S401, determining the fuzzy standard deviation σ;

[0024] S402: Establish a Gaussian blur model.

[0025] Preferably, in S401, the calculation formula of the fuzzy standard deviation is:

[0026] σ=k·d·tan(θ)

[0027] Where k is a constant set according to the actual situation, d is the actual distance between the projector and the projection surface, and θ is the projection angle;

[0028] In S402, the calculation formula for calculating the fuzzy model is:

[0029]

[0030] Where x is the horizontal displacement from the blur center, y is the vertical displacement from the blur center, σ is the standard deviation of the Gaussian distribution, and e is the base of the natural logarithm.

[0031] Preferably, in S5, the calculation formula for the blur kernel calculation is as follows:

[0032]

[0033] Where θ is the projection angle, which represents the angle between the projector and the projection surface, cos(θ) is the cosine of the projection, and sin(θ) is the sine of the projection angle.

[0034] Preferably, in S6, the following sub-steps are included:

[0035] S601, performing Fourier transform on the captured image;

[0036] S602, using a Wiener filter to restore the image;

[0037] S603: Calculate the inverse Fourier transform of the restored image to obtain a corrected image.

[0038] Preferably, in S601, the calculation formula of the Fourier transform is:

[0039]

[0040] Among them, K(x, y) is the value of the blur kernel in the spatial domain, x is the horizontal coordinate of the blur kernel in the spatial domain, y is the vertical coordinate of the blur kernel in the spatial domain, u is the horizontal coordinate in the frequency domain, v is the vertical coordinate in the frequency domain, and j is the imaginary unit.

[0041] Preferably, in S603, the calculation formula for restoring the image by the Wiener filter is:

[0042]

[0043] Wherein, I(x, y) is the value of the captured original projection image at the point (x, y), F(I(x, y)) is the frequency domain of the original image, and K is the regularization parameter.

[0044] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0045] 1. Through precise data collection, angle adjustment, model building and image restoration steps, the clarity of the projected image during side projection is significantly improved, making the adjustment process more objective and accurate, which helps to ensure the image quality of the final correction result.

[0046] Second, the influence of various projection conditions on the correction effect is compensated by adding environmental impact coefficients, which enhances the adaptability and flexibility of the correction method. The correction method can be appropriately adjusted according to different application requirements and environmental conditions to obtain the best correction effect.

[0047] Third, through the Gaussian blur model, and considering other types of blur models for comparison and selection, the model's ability to simulate and restore image blur is improved. The blur kernel is trained with deep learning methods, which improves the calculation accuracy and adaptability of the blur kernel, providing a more accurate basis for image restoration.

[0048] Fourth, by using the image processing algorithm of the Wiener filter in the image restoration process, the fineness and accuracy of image restoration are improved through methods such as adaptive optimization of regularization parameters. The introduction of Fourier transform and inverse Fourier transform enables the image to be converted between the frequency domain and the spatial domain, facilitating more complex image processing operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which this application can be implemented. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in this application without affecting the effects and purposes that can be achieved by this application.

[0053] Example

[0054] See also Figure 1 The present invention provides a technical solution: a method for correcting the blur of side projection of a projector, comprising the following steps:

[0055] S1. Data collection: including the following sub-steps:

[0056] S101. Use the same test pattern to take multiple images at different side projection angles. Select test patterns that contain a variety of lines, edges, and details, such as checkerboards, sine wave lines, etc., to fully evaluate image clarity. Ensure that the shooting environment is stable and avoid external light interference that affects image quality. Use a high-resolution camera or a professional camera to ensure image accuracy. Select multiple side projection angles (e.g., 0°, 15°, 30°, 45°, etc.).

[0057] S102, scoring the clarity of each image based on factors such as the degree of detail retention, edge sharpness, and contrast of the image;

[0058] S103, recording each side projection angle and the corresponding image clarity score, recording each side projection angle, the corresponding image clarity score and shooting time and other information, and storing the recorded data in a database for subsequent analysis and processing.

[0059] S2, calculating the side projection angle adjustment value;

[0060] The calculation formula for side projection angle adjustment is:

[0061]

[0062] Among them, H is the height of the projected image, D is the distance between the projector and the projection screen, and α is the initial vertical angle. The accuracy of the adjustment value can be verified by fine-tuning the projector angle and observing the image changes. Correction can be made according to various projection conditions, such as considering factors such as ambient light, screen material, and distance changes. In the calculation formula, an environmental impact coefficient can be added to adjust the projection angle more accurately.

[0063] S3, adjust the projector angle;

[0064] According to the calculation formula result of the angle adjustment, the angle of the projector lens is adjusted so that the new vertical angle is α+Δα. According to the calculated adjustment value Δα, the angle of the projector lens is gradually adjusted to ensure the accuracy of the projector angle during the adjustment process to avoid over-adjustment or under-adjustment. The electric pan / tilt or servo mechanism can be used to automatically adjust the angle of the projector to improve the adjustment accuracy and response speed. In addition, a feedback system can be set to monitor the clarity of the adjusted image in real time to further optimize the angle adjustment.

[0065] S4, establishing a model; including the following sub-steps:

[0066] S401, determining the fuzzy standard deviation σ;

[0067] The calculation formula of fuzzy standard deviation is:

[0068] σ=k·d·tan(θ)

[0069] Where k is a constant set according to the actual situation, d is the actual distance between the projector and the projection surface, and θ is the projection angle;

[0070] The blur standard deviation may change under different environments and projection conditions, so it is possible to consider using an adaptive algorithm to dynamically update the calculation of the standard deviation based on real-time collected data.

[0071] S402, establish a Gaussian fuzzy model, and calculate the calculation formula of the fuzzy model as follows:

[0072]

[0073] Where x is the horizontal displacement from the blur center, y is the vertical displacement from the blur center, σ is the standard deviation of the Gaussian distribution, and e is the base of the natural logarithm.

[0074] It can be combined with other types of blur models (such as motion blur and defocus blur) for comparison, and the blur model that best suits the current conditions can be selected to improve the restoration effect.

[0075] S5, fuzzy kernel calculation;

[0076] The calculation formula for the fuzzy kernel is as follows:

[0077]

[0078] Where θ is the projection angle, which represents the angle between the projector and the projection surface, cos(θ) is the cosine of the projection, and sin(θ) is the sine of the projection angle.

[0079] Consider combining deep learning methods to train blur kernels to improve the calculation accuracy of blur kernels. By building a data set, the model can learn image features under different blur conditions, thereby automatically generating a better blur kernel.

[0080] S6, image restoration;

[0081] The following sub-steps are included:

[0082] S601, perform Fourier transform on the captured image; before the Fourier transform, preprocessing (such as denoising) can be performed to reduce the influence of noise on the transform result. At the same time, the fast Fourier transform (FFT) algorithm can be considered to accelerate the calculation process.

[0083] The calculation formula of Fourier transform is:

[0084]

[0085] Among them, K(x, y) is the value of the blur kernel in the spatial domain, x is the horizontal coordinate of the blur kernel in the spatial domain, y is the vertical coordinate of the blur kernel in the spatial domain, u is the horizontal coordinate in the frequency domain, v is the vertical coordinate in the frequency domain, and j is the imaginary unit.

[0086] The captured image is subjected to Fourier transform to convert it from the spatial domain to the frequency domain, and the frequency domain representation of the image is obtained for subsequent processing.

[0087] S602, restore the image using a Wiener filter; the performance of the Wiener filter depends on the estimation of the signal-to-noise ratio (SNR), so the regularization parameter K can be optimized by an adaptive method to make the image restoration more refined.

[0088] According to actual needs, the parameters of the Wiener filter, such as the regularization parameter K, are reasonably set, and the frequency domain image is restored using the Wiener filter to obtain a restored frequency domain image.

[0089] S603, calculating the inverse Fourier transform of the restored image to obtain a corrected image. After the inverse Fourier transform, image post-processing steps such as sharpening and contrast adjustment can be added to further improve the visual quality of the restored image.

[0090] The calculation formula for restoring the image using the Wiener filter is:

[0091]

[0092] Wherein, I(x, y) is the value of the captured original projection image at the point (x, y), F(I(x, y)) is the frequency domain of the original image, and K is the regularization parameter.

[0093] The restored frequency domain image is inversely Fourier transformed to convert it from the frequency domain back to the spatial domain to obtain a corrected image, which is improved in clarity and detail retention.

[0094] S7. Output the corrected image.

[0095] The output corrected image can be saved in a variety of formats to suit different application requirements. At the same time, a real-time preview function can be provided, allowing users to quickly view the correction effect during actual use. In addition, an image quality evaluation report can be provided to help users understand the correction effect and further process the output image, such as cropping, resizing, etc., to meet actual application requirements.

[0096] Precise correction methods and high-quality output images enhance the user's visual experience when viewing projected images. The automated and intelligent correction process reduces the user's operating difficulty and time cost, and improves overall efficiency and satisfaction. The image quality evaluation report provides users with detailed correction effect information, which helps them to further process and apply the output image.

[0097] Improve the practicality, accuracy and operability of the entire projector side projection blur correction method. By introducing modern technical means such as adaptive algorithms, machine learning, and real-time feedback, the correction effect can be further optimized to achieve the best image quality of the projector.

[0098] In summary, through precise data acquisition, angle adjustment, model building and image restoration steps, the clarity of the projected image during side projection is significantly improved, making the adjustment process more objective and accurate, which helps to ensure the image quality of the final correction result.

[0099] By taking into account the influence of various projection conditions on the correction effect and compensating by adding environmental impact coefficients, the adaptability and flexibility of the correction method are enhanced. The correction method can be appropriately adjusted according to different application requirements and environmental conditions to obtain the best correction effect.

[0100] By using the Gaussian blur model and considering other types of blur models for comparison and selection, the model's ability to simulate and restore image blur is improved. The blur kernel is trained using deep learning methods to improve the calculation accuracy and adaptability of the blur kernel, providing a more accurate basis for image restoration.

[0101] In the process of image restoration, the image processing algorithm of Wiener filter is used, and methods such as adaptive optimization of regularization parameters are used to improve the fineness and accuracy of image restoration. The introduction of Fourier transform and inverse Fourier transform enables the image to be converted between the frequency domain and the spatial domain, which facilitates more complex image processing operations.

[0102] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present invention may be combined and / or combined in various ways, even if such combinations and / or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments and / or claims of the present invention may be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All of these combinations and / or combinations fall within the scope of the present invention.

Claims

1. A method for correcting blur of side projection of a projector, characterized in that: The following steps are involved: S1, data collection; S2, calculating the side projection angle adjustment value; S3, adjust the projector angle; S4, build model; S5, fuzzy kernel calculation; S6, image restoration; S7. Output the corrected image.

2. The method for correcting side projection blur of a projector according to claim 1, characterized in that: In S1, the following sub-steps are included: S101, capturing multiple images using the same test pattern at different side projection angles; S102, scoring the clarity of each image; S103, recording each side projection angle and the corresponding image clarity score.

3. The method for correcting side projection blur of a projector according to claim 1, characterized in that: In S2, the calculation formula for adjusting the side projection angle is: Where H is the height of the projected image, D is the distance between the projector and the projection screen, and α is the initial vertical angle.

4. The method for correcting side projection blur of a projector according to claim 3, characterized in that: In S3, according to the calculation formula result of the angle adjustment, the angle of the projector lens is adjusted so that the new vertical angle is α+Δα.

5. The method for correcting side projection blur of a projector according to claim 1, characterized in that: In S4, the following sub-steps are included: S401, determining the fuzzy standard deviation σ; S402: Establish a Gaussian blur model.

6. The method for correcting side projection blur of a projector according to claim 5, characterized in that: In S401, the calculation formula of the fuzzy standard deviation is: σ=k·d·tan(θ) Where k is a constant set according to the actual situation, d is the actual distance between the projector and the projection surface, and θ is the projection angle; In S402, the calculation formula for calculating the fuzzy model is: Where x is the horizontal displacement from the blur center, y is the vertical displacement from the blur center, σ is the standard deviation of the Gaussian distribution, and e is the base of the natural logarithm.

7. The method for correcting side projection blur of a projector according to claim 1, characterized in that: In S5, the calculation formula of the blur kernel calculation is as follows: Where θ is the projection angle, which represents the angle between the projector and the projection surface, cos(θ) is the cosine of the projection, and sin(θ) is the sine of the projection angle.

8. The method for correcting side projection blur of a projector according to claim 1, characterized in that: In S6, the following sub-steps are included: S601, performing Fourier transform on the captured image; S602, using a Wiener filter to restore the image; S603: Calculate the inverse Fourier transform of the restored image to obtain a corrected image.

9. The method for correcting side projection blur of a projector according to claim 8, characterized in that: In S601, the calculation formula of the Fourier transform is: Among them, K(x, y) is the value of the blur kernel in the spatial domain, x is the horizontal coordinate of the blur kernel in the spatial domain, y is the vertical coordinate of the blur kernel in the spatial domain, u is the horizontal coordinate in the frequency domain, v is the vertical coordinate in the frequency domain, and j is the imaginary unit.

10. The method for correcting side projection blur of a projector according to claim 9, characterized in that: In S603, the calculation formula for restoring the image using the Wiener filter is: Wherein, I(x, y) is the value of the captured original projection image at the point (x, y), F(I(x, y)) is the frequency domain of the original image, and K is the regularization parameter.

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