CN112381739A - Imaging distortion correction method and device of AR-HUD system - Google Patents
Imaging distortion correction method and device of AR-HUD system Download PDFInfo
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Abstract
The invention discloses an imaging distortion correction method and device of an AR-HUD system, which are characterized in that a matrix is quickly searched according to coordinate offset of pre-constructed pixel coordinates of a pre-distorted image relative to pixel coordinates of a normal image, pixel coordinates of the pre-distorted image corresponding to the pixel coordinates of each normal image input by the AR-HUD system are calculated, color information of the pixel points of the normal image is copied to the corresponding pixel points of the pre-distorted image to obtain an initial pre-distorted image, color filling is carried out on each cavity point of the initial pre-distorted image relative to the normal image by adopting a neighborhood interpolation method to obtain the pre-distorted image, and the pre-distorted image is projected through the AR-HUD system to obtain a target image after imaging distortion correction is carried out on the normal image. Because the method carries out distortion correction on the imaging of the AR-HUD system, the AR-HUD system can finally present a normal undistorted image on the front windshield of the automobile.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an imaging distortion correction method and device of an AR-HUD system.
Background
The AR-HUD (Augmented Reality Head Up Display) system is used for reasonably and vividly projecting driving information such as navigation, driving speed, driving lane lines and the like onto a front windshield of an automobile according to actual traffic road conditions and a driver sight line area by combining an Augmented Reality technology and a Head-Up Display technology, thereby reducing the driver's distraction to a great extent, reducing the driver's visual fatigue, and improving the convenience and safety of driving.
However, in the process of projecting the image containing the driving information onto the front windshield of the automobile by the AR-HUD system, the image needs to undergo complex optical conversion, and the front windshield of the automobile is of a non-standard surface type, so that the image finally presented on the front windshield of the automobile by the AR-HUD system is distorted to some extent, thereby affecting the visual effect.
In conclusion, how to correct the distortion of the image of the AR-HUD system so that the AR-HUD system finally presents a normal undistorted image on the front windshield of the automobile becomes a technical problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention discloses an imaging distortion correction method and device for an AR-HUD system, so as to perform distortion correction on imaging of the AR-HUD system, and enable the AR-HUD system to finally present a normal undistorted image on a front windshield of an automobile.
An imaging aberration correction method of an AR-HUD system, comprising:
quickly searching a matrix according to the coordinate offset of the pre-constructed pixel point coordinate of the pre-distorted image relative to the pixel point coordinate of the normal image, and calculating the pixel point coordinate of the pre-distorted image corresponding to the pixel point coordinate of each normal image in the normal image input by the AR-HUD system;
copying the color information of the normal image pixel points to the corresponding pre-distortion image pixel points to obtain an initial pre-distortion image;
carrying out color filling on each hole point of the initial pre-distortion image relative to the normal image by adopting a neighborhood interpolation method to obtain a pre-distortion image;
and projecting the pre-distortion image through the AR-HUD system to obtain a target image after the normal image is subjected to imaging distortion correction.
Optionally, the copying the color information of the normal image pixel point to the corresponding pre-distortion image pixel point to obtain an initial pre-distortion image specifically includes:
when the pixel point coordinates of the pre-distorted image are non-integers, the non-integer pixel point coordinates of the pre-distorted image are converted into integer pixel point coordinates of the pre-distorted image by adopting a nearest neighbor interpolation method; quickly searching a matrix according to the coordinate offset to obtain color information of a normal image pixel point corresponding to the integer pre-distorted image pixel point and copying the color information to the non-integer pre-distorted image pixel point;
when the pixel point coordinates of the predistortion image are integers, the color information of the normal image pixel points in the normal image corresponding to the pixel point coordinates of the predistortion image of the integers is directly copied to the pixel points of the predistortion image of the integers.
Optionally, the construction process of the coordinate offset fast lookup matrix is as follows:
acquiring normal image sampling point coordinates of a normal image sampling point in a physical coordinate system and distorted image sampling point coordinates of a distorted image sampling point in the physical coordinate system by using a pre-established AR-HUD system model, wherein the normal image sampling point corresponds to the distorted image sampling point;
transforming each normal image sampling point coordinate from a physical coordinate system to a pixel coordinate system to obtain a corresponding normal image pixel point coordinate, and transforming each distorted image sampling point coordinate from the physical coordinate system to the pixel coordinate system to obtain a corresponding distorted image pixel point coordinate;
and calculating the coordinate offset of the distorted image sampling point relative to the normal image sampling point based on the distorted image pixel point coordinates and the corresponding normal image pixel point coordinates to obtain the coordinate offset fast search matrix.
Optionally, the calculating a coordinate offset of the distorted image sampling point relative to the normal image sampling point based on the distorted image pixel point coordinates and the corresponding normal image pixel point coordinates to obtain the coordinate offset fast search matrix specifically includes:
based on the distribution rule of pixel point coordinates of each normal image, partitioning the normal image by using a grid method to obtain a plurality of sub-images;
respectively solving the coordinate offset of a non-sampling point in each sub-image according to the coordinate offset of a normal image sampling point in each sub-image;
and according to the coordinate offset of the normal image sampling points corresponding to all the sub-images in the normal image and the coordinate offset of the non-sampling points, constructing a coordinate offset fast search matrix of the pre-distorted image relative to the normal image.
Optionally, when the number of the normal image sampling points is 16, the process of obtaining the offset of the non-sampling point P ═ D (i + dx, j + dy) according to the coordinate offset of the 16 normal image sampling points includes:
the 16 normal image sampling points are respectively:
in the formula, xiAnd yjRespectively the abscissa and ordinate of the sampling point closest to the non-sampling point, i is the row number, and j is the column number;
solving a coordinate offset matrix X in the X direction and a coordinate offset matrix Y in the Y direction between the 16 normal image sampling points and the corresponding pre-distorted image sampling points;
the coordinate offset quantity Deltadx in the X direction and the coordinate offset quantity Deltady in the Y direction of the non-sampling point P ═ D (i + dx, j + dy) are obtained according to the coordinate offset matrix X and the coordinate offset matrix Y.
An imaging aberration correcting device of an AR-HUD system, comprising:
the calculation unit is used for quickly searching a matrix according to the coordinate offset of the pixel point coordinates of the pre-distorted image relative to the pixel point coordinates of the normal image, and calculating the pixel point coordinates of the pre-distorted image corresponding to the pixel point coordinates of each normal image in the normal image input by the AR-HUD system;
the information copying unit is used for copying the color information of the normal image pixel points to the corresponding pre-distorted image pixel points to obtain an initial pre-distorted image;
the color filling unit is used for performing color filling on each hole point of the initial pre-distorted image relative to the normal image by adopting a neighborhood interpolation method to obtain a pre-distorted image;
and the projection unit is used for projecting the pre-distortion image through the AR-HUD system to obtain a target image after the normal image is subjected to imaging distortion correction.
Optionally, the information copying unit specifically includes:
the first information replication sub-unit is used for converting the non-integer pixel coordinates of the pre-distorted image into integer pixel coordinates of the pre-distorted image by adopting a nearest neighbor interpolation method when the pixel coordinates of the pre-distorted image are non-integers; quickly searching a matrix according to the coordinate offset to obtain color information of a normal image pixel point corresponding to the integer pre-distorted image pixel point and copying the color information to the non-integer pre-distorted image pixel point;
and the second information replication sub-unit is used for directly replicating the color information of the normal image pixel point corresponding to the integer predistortion image pixel point coordinate in the normal image to the integer predistortion image pixel point when the predistortion image pixel point coordinate is an integer.
Optionally, the method further includes: a matrix construction unit;
the matrix construction unit specifically includes:
the system comprises an acquisition subunit, a processing subunit and a processing unit, wherein the acquisition subunit is used for acquiring the coordinates of a normal image sampling point in a physical coordinate system and the coordinates of a distorted image sampling point in the physical coordinate system by using a pre-established AR-HUD system model, and the normal image sampling point corresponds to the distorted image sampling point;
the coordinate system transformation subunit is used for transforming the coordinates of each normal image sampling point to a pixel coordinate system from a physical coordinate system to obtain corresponding coordinates of a normal image pixel point, and transforming the coordinates of each distorted image sampling point to the pixel coordinate system from the physical coordinate system to obtain corresponding coordinates of a distorted image pixel point;
and the calculation subunit is used for calculating the coordinate offset of the distorted image sampling point relative to the normal image sampling point based on the distorted image pixel point coordinates and the corresponding normal image pixel point coordinates, so as to obtain the coordinate offset fast search matrix.
Optionally, the computing subunit is specifically configured to:
based on the distribution rule of pixel point coordinates of each normal image, partitioning the normal image by using a grid method to obtain a plurality of sub-images;
respectively solving the coordinate offset of a non-sampling point in each sub-image according to the coordinate offset of a normal image sampling point in each sub-image;
and according to the coordinate offset of the normal image sampling points corresponding to all the sub-images in the normal image and the coordinate offset of the non-sampling points, constructing a coordinate offset fast search matrix of the pre-distorted image relative to the normal image.
Optionally, the matrix building unit further includes: an offset amount operator unit;
the offset amount operator unit is specifically configured to:
when the number of the normal image sampling points is 16, the process of obtaining the offset of the non-sampling point P ═ D (i + dx, j + dy) according to the coordinate offset of the 16 normal image sampling points includes:
the 16 normal image sampling points are respectively:
in the formula, xiAnd yjRespectively the closest sampling point to the non-sampling pointCoordinates and ordinate, i is row number, j is column number;
solving a coordinate offset matrix X in the X direction and a coordinate offset matrix Y in the Y direction between the 16 normal image sampling points and the corresponding pre-distorted image sampling points;
the coordinate offset quantity Deltadx in the X direction and the coordinate offset quantity Deltady in the Y direction of the non-sampling point P ═ D (i + dx, j + dy) are obtained according to the coordinate offset matrix X and the coordinate offset matrix Y.
According to the technical scheme, the invention discloses an imaging distortion correction method and device of an AR-HUD system, a matrix is quickly searched according to coordinate offset of pixel coordinates of a pre-distorted image relative to pixel coordinates of a normal image, pixel coordinates of the pre-distorted image corresponding to the pixel coordinates of each normal image in the normal image input by the AR-HUD system are calculated, color information of the pixel points of the normal image is copied to the corresponding pixel points of the pre-distorted image to obtain an initial pre-distorted image, color filling is carried out on each hole point of the initial pre-distorted image relative to the normal image by adopting a neighborhood interpolation method to obtain the pre-distorted image, and the pre-distorted image is projected by the AR-HUD system to obtain a target image after imaging distortion correction is carried out on the normal image. Because the method carries out distortion correction on the imaging of the AR-HUD system, the AR-HUD system can finally present a normal undistorted image on the front windshield of the automobile, thereby solving the problems in the prior art.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the disclosed drawings without creative efforts.
FIG. 1 is a flowchart of an imaging aberration correction method for an AR-HUD system according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for constructing a coordinate offset fast lookup matrix according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of coordinates of sampling points corresponding to a normal image and a distorted image, which are obtained through an AR-HUD system model established by Zemax software according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a non-sampling point coordinate offset solution according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating an AR-HUD system imaging predistortion principle disclosed in an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of an imaging aberration correcting apparatus of an AR-HUD system according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a matrix building unit according to an embodiment of the present invention.
Detailed Description
In the prior art, the scheme for performing distortion correction on the imaging of the AR-HUD system mainly realizes the distortion correction of the image from a software layer, and the correction process specifically comprises the following steps: establishing a distortion model according to the distortion process of the AR-HUD system by the distorted image; and solving distortion parameters of the AR-HUD system, solving a predistortion image by using the distortion parameters and the distortion model, and finally performing distortion correction on the imaging of the AR-HUD system by using the predistortion image.
The inventor of this patent finds after studying that current image distortion correction scheme is used for correcting symmetrical distortion more, however, the optical system of AR-HUD system adopts the off-axis design for AR-HUD system's formation of image distortion has irregular and asymmetric characteristics, promptly, AR-HUD system's image distortion is asymmetric distortion, consequently, current image distortion correction scheme can't be accurate correction asymmetric distortion.
The embodiment of the invention discloses an imaging distortion correction method and device of an AR-HUD system, which comprises the steps of quickly searching a matrix according to coordinate offset of pre-constructed pixel coordinates of a pre-distorted image relative to pixel coordinates of a normal image, calculating pixel coordinates of the pre-distorted image corresponding to the pixel coordinates of each normal image in the normal image input by the AR-HUD system, copying color information of the pixel points of the normal image to the corresponding pixel points of the pre-distorted image to obtain an initial pre-distorted image, performing color filling on each cavity point of the initial pre-distorted image relative to the normal image by adopting a neighborhood interpolation method to obtain a pre-distorted image, and projecting the pre-distorted image through the AR-HUD system to obtain a target image after imaging distortion correction of the normal image. Because the method carries out distortion correction on the imaging of the AR-HUD system, the AR-HUD system can finally present a normal undistorted image on the front windshield of the automobile, thereby solving the problems in the prior art.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention discloses a flowchart of an imaging aberration correction method for an AR-HUD system, where the method includes:
s101, quickly searching a matrix according to coordinate offset of pre-constructed pixel coordinates of the pre-distorted image relative to pixel coordinates of the normal image, and calculating the pixel coordinates of the pre-distorted image corresponding to the pixel coordinates of each normal image in the normal image input by the AR-HUD system;
the normal image in the present embodiment refers to: the automobile camera collects undistorted real-time images in the driving process.
In this embodiment, after acquiring the normal image input by the AR-HUD system at the current time, the normal image is pre-distorted, and the pre-distortion process is as follows: determining the coordinates of each normal image pixel point in the normal image; searching coordinate offset of the pre-distorted image corresponding to each normal image pixel point coordinate relative to the normal image from the coordinate offset fast search matrix; obtaining a pre-distortion image pixel point coordinate corresponding to the normal image pixel point coordinate according to the normal image pixel point coordinate and the searched corresponding coordinate offset; after the pixel point coordinates of the pre-distorted image corresponding to the pixel point coordinates of each normal image in the normal image are obtained, the corresponding relation of the pixel point coordinates of the pre-distorted image and the normal image can be established.
S102, copying color information of the normal image pixel points to corresponding pre-distortion image pixel points to obtain an initial pre-distortion image;
specifically, when the pixel coordinates of the pre-distorted image are non-integers, the non-integer pixel coordinates of the pre-distorted image are converted into integer pixel coordinates of the pre-distorted image by adopting a nearest neighbor interpolation method; quickly searching the matrix according to the coordinate offset to obtain color information of normal image pixel points corresponding to the integer pre-distortion image pixel points and copying the color information to non-integer pre-distortion image pixel points;
when the pixel point coordinates of the predistortion image are integers, the color information of the normal image pixel points in the normal image corresponding to the pixel point coordinates of the predistortion image of the integers is directly copied to the positions corresponding to the pixel points of the predistortion image of the integers.
In this embodiment, an initial pre-distorted image is generated to move the distortion point of the normal image during the projection process to the correct position when copying the color information.
Step S103, filling color of each hole point of the initial pre-distorted image relative to the hole points of the normal image by adopting a neighborhood interpolation method to obtain a pre-distorted image;
it should be noted that, because the distorted image may amplify a part of the normal image, in the amplified region, the normal image pixels are significantly less than the distorted image pixels, so that the normal image pixels and the distorted image pixels cannot be in one-to-one correspondence, and there may be some corresponding points in the distorted image that are not in the normal image, and these points are void points.
Because the hole point has no color information, interpolation processing needs to be performed on the hole point, and the interpolation method is as follows: and calculating the average value of the color information of all non-hole points in the preset neighborhood range of the hole point in the pre-distorted image, taking the calculated average value as the color information of the hole point, and filling the color of the hole point.
And S104, projecting the pre-distorted image through an AR-HUD system to obtain a target image after imaging distortion correction is carried out on the normal image.
In summary, the imaging distortion correction method of the AR-HUD system disclosed by the invention includes the steps of quickly searching a matrix according to coordinate offset of pre-constructed pixel coordinates of a pre-distorted image relative to pixel coordinates of a normal image, calculating pixel coordinates of the pre-distorted image corresponding to the pixel coordinates of each normal image in the normal image input by the AR-HUD system, copying color information of the pixel points of the normal image to the corresponding pixel points of the pre-distorted image to obtain an initial pre-distorted image, performing color filling on each hole point of the initial pre-distorted image relative to the normal image by adopting a neighborhood interpolation method to obtain the pre-distorted image, and projecting the pre-distorted image through the AR-HUD system to obtain a target image after imaging distortion correction is performed on the normal image. Because the method carries out distortion correction on the imaging of the AR-HUD system, the AR-HUD system can finally present a normal undistorted image on the front windshield of the automobile, thereby solving the problems in the prior art.
Referring to fig. 2, a flowchart of a method for constructing a coordinate offset fast lookup matrix disclosed in the embodiment of the present invention includes:
step S201, acquiring normal image sampling point coordinates of a normal image sampling point in a physical coordinate system and distorted image sampling point coordinates of a distorted image sampling point in the physical coordinate system by using a pre-established AR-HUD system model;
wherein the normal image sampling points correspond to the distorted image sampling points.
In practical application, Zemax software can be used for establishing the AR-HUD system model, and the specific establishing process can refer to the existing scheme, which is not described herein again.
In this embodiment, can utilize AR-HUD system model to track light through the light trajectory of AR-HUD system, acquire the normal image sampling point coordinate of normal image sampling point under the physical coordinate system to and the distortion image sampling point coordinate of distortion image sampling point under the physical coordinate system.
The coordinates of each normal image sampling point in the normal image and the corresponding physical position of each distorted image sampling point in the distorted image in the physical coordinate system can be seen in fig. 3, the normal image sampling point is a sampling point located on the grid, and the distorted image sampling point is a sampling point located outside the grid.
Step S202, transforming each normal image sampling point coordinate from a physical coordinate system to a pixel coordinate system to obtain a corresponding normal image pixel point coordinate, and transforming each distorted image sampling point coordinate from the physical coordinate system to the pixel coordinate system to obtain a corresponding distorted image pixel point coordinate;
the origin of a pixel coordinate system u-v is O0, the abscissa u and the ordinate v are rows and columns where the image is located respectively, and in the visual processing library OpenCV, u corresponds to x and v corresponds to y.
Step S203, calculating coordinate offset of a distorted image sampling point relative to a normal image sampling point based on the distorted image pixel point coordinates and the corresponding normal image pixel point coordinates, and obtaining a coordinate offset fast search matrix.
The implementation process of step S203 may specifically include:
(1) based on the distribution rule of pixel point coordinates of each normal image, partitioning the normal image by using a grid method to obtain a plurality of sub-images;
the grid method used in this step can be seen in fig. 3.
(2) Respectively solving the coordinate offset of a non-sampling point in each sub-image according to the coordinate offset of a normal image sampling point in each sub-image;
wherein the coordinate offset includes: the amount of coordinate shift in the x-direction and the amount of coordinate shift in the y-direction.
(3) And constructing a coordinate offset fast search matrix of the pre-distortion image relative to the normal image according to the coordinate offset of the normal image sampling points corresponding to all the sub-images in the normal image and the coordinate offset of the non-sampling points.
The method of finding the x-direction coordinate offset will be described in detail below.
In practical application, an interpolation method of the BiCubic basis function can be adopted, and the coordinate offset of the non-sampling point in each sub-image is respectively obtained according to the coordinate offset of the corresponding neighborhood sampling point in each sub-image.
Referring to the schematic diagram of coordinate offset determination of non-sampling points shown in fig. 4, it is assumed that the process of determining the offset of non-sampling point P ═ D (i + dx, j + dy) from the coordinate offsets of 16 normal image sampling points includes:
the 16 normal image sampling points are respectively:
in the formula, xiAnd yjRespectively the abscissa and ordinate of the sampling point closest to the non-sampling point, i is a row sequence number, and j is a list number;
solving a coordinate offset matrix X in the X direction and a coordinate offset matrix Y in the Y direction between 16 normal image sampling points and the corresponding pre-distorted image sampling points;
the coordinate offset quantity Deltadx in the X direction and the coordinate offset quantity Deltady in the Y direction of the non-sampling point P ═ D (i + dx, j + dy) are obtained according to the coordinate offset matrix X and the coordinate offset matrix Y.
The X-direction coordinate offset matrix X corresponding to each normal image sampling point and each distorted image sampling point is as follows:
then, the coordinate offset Δ dx of the non-sampling point P ═ D (i + dx, j + dy) in the X direction can be obtained from the coordinate offset matrix X as follows:
where dx is the distance of point D (i + dx, j + dy) in the X direction relative to point D (i, j), dy is the distance of point D (i + dx, j + dy) in the y direction relative to point D (i, j), the intermediate matrix is the X-direction offset matrix X of the 16 normal image sample points adjacent to D (i + dx, j + dy), s (X) is the BiCubic basis function, which is a cubic function approximating the theoretical optimal interpolation function sin (pi X)/X, and the expression of s (X) is as follows:
in the formula, the optimal value of a is-0.5;
the coordinate offset Δ dy of the non-sampling point P ═ D (i + dx, j + dy) in the y direction is calculated as follows:
in a similar manner to the calculation of X, the coordinate offset matrix Y in the Y direction corresponding to each normal image sampling point is:
then, the coordinate offset Δ dy of the non-sampling point P ═ D (i + dx, j + dy) in the Y direction can be obtained from the coordinate offset matrix Y as follows:
where dx is the distance of point D (i + dx, j + dy) in the x direction relative to point D (i, j), dy is the distance of point D (i + dx, j + dy) in the Y direction relative to point D (i, j), the intermediate matrix is the Y-direction offset matrix Y of the 16 normal-image sample points to which D (i + dx, j + dy) is adjacent, and s (x) is the BiCubic basis function, which is a cubic function approximating the theoretical optimal interpolation function sin (pi x)/x, the expression of s (x) is as follows:
in the formula, the optimal value of a is-0.5.
In summary, the imaging distortion correction method of the AR-HUD system disclosed by the invention includes the steps of quickly searching a matrix according to coordinate offset of pre-constructed pixel coordinates of a pre-distorted image relative to pixel coordinates of a normal image, calculating pixel coordinates of the pre-distorted image corresponding to the pixel coordinates of each normal image in the normal image input by the AR-HUD system, copying color information of the pixel points of the normal image to the corresponding pixel points of the pre-distorted image to obtain an initial pre-distorted image, performing color filling on each hole point of the initial pre-distorted image relative to the normal image by adopting a neighborhood interpolation method to obtain the pre-distorted image, and projecting the pre-distorted image through the AR-HUD system to obtain a target image after imaging distortion correction is performed on the normal image. Because the method carries out distortion correction on the imaging of the AR-HUD system, the AR-HUD system can finally present a normal undistorted image on the front windshield of the automobile, thereby solving the problems in the prior art.
In addition, the method for constructing the coordinate offset of the pre-distortion image relative to the normal image to quickly search the matrix can reduce the real-time influence and data calculation amount of an operation algorithm on an AR-HUD system. And the correction accuracy is improved by calculating the coordinate offset of the distorted image sampling point relative to the normal image sampling point.
Further, compared with the distortion correction method of the camera in the existing scheme, the method does not need to obtain the camera parameters to carry out distortion correction, and can realize the distortion correction only through simple calibration, so that the asymmetric distortion can be corrected well.
In order to facilitate understanding of the imaging distortion correction method of the AR-HUD system disclosed by the invention, referring to an AR-HUD system predistortion principle schematic diagram shown in FIG. 5, in the prior art, a normal image is projected by the AR-HUD system and then becomes a distorted image.
Corresponding to the embodiment of the method, the invention also discloses an imaging distortion correction device of the AR-HUD system.
Referring to fig. 6, a schematic structural diagram of an imaging aberration correction apparatus of an AR-HUD system according to an embodiment of the present invention includes:
the calculation unit 301 is configured to quickly find a matrix according to a coordinate offset of a pre-constructed pixel coordinate of the pre-distorted image relative to a pixel coordinate of the normal image, and calculate a pixel coordinate of the pre-distorted image corresponding to a pixel coordinate of each normal image in the normal image input by the AR-HUD system;
the normal image in the present embodiment refers to: the automobile camera collects undistorted real-time images in the driving process.
In this embodiment, after acquiring the normal image input by the AR-HUD system at the current time, the normal image is pre-distorted, and the pre-distortion process is as follows: determining the coordinates of each normal image pixel point in the normal image; searching coordinate offset of the pre-distorted image corresponding to each normal image pixel point coordinate relative to the normal image from the coordinate offset fast search matrix; obtaining a pre-distortion image pixel point coordinate corresponding to the normal image pixel point coordinate according to the normal image pixel point coordinate and the searched corresponding coordinate offset; after the pixel point coordinates of the pre-distorted image corresponding to the pixel point coordinates of each normal image in the normal image are obtained, the corresponding relation of the pixel point coordinates of the pre-distorted image and the normal image can be established.
The information copying unit 302 is configured to copy color information of a normal image pixel point to a corresponding pre-distorted image pixel point to obtain an initial pre-distorted image;
the information copying unit 302 may specifically include:
the first information replication sub-unit is used for converting the non-integer pixel coordinates of the pre-distorted image into integer pixel coordinates of the pre-distorted image by adopting a nearest neighbor interpolation method when the pixel coordinates of the pre-distorted image are non-integers; quickly searching the matrix according to the coordinate offset to obtain color information of normal image pixel points corresponding to the integer pre-distortion image pixel points and copying the color information to non-integer pre-distortion image pixel points;
and the second information replication sub-unit is used for directly replicating the color information of the normal image pixel point corresponding to the integer pre-distortion image pixel point coordinate in the normal image to the integer pre-distortion image pixel point when the pre-distortion image pixel point coordinate is the integer.
The color filling unit 303 is configured to perform color filling on each hole point existing in the initial pre-distorted image relative to the normal image by using a neighborhood interpolation method to obtain a pre-distorted image;
it should be noted that, because the distorted image may amplify a part of the normal image, in the amplified region, the normal image pixels are significantly less than the distorted image pixels, so that the normal image pixels and the distorted image pixels cannot be in one-to-one correspondence, and there may be some corresponding points in the distorted image that are not in the normal image, and these points are void points.
Because the hole point has no color information, interpolation processing needs to be performed on the hole point, and the interpolation method is as follows: and calculating the average value of the color information of all non-hole points in the preset neighborhood range of the hole point, taking the calculated average value as the color information of the hole point, and filling the color of the hole point.
And the projection unit 304 is used for projecting the pre-distorted image through an AR-HUD system to obtain a target image after imaging distortion correction is carried out on the normal image.
In summary, the imaging distortion correction device of the AR-HUD system disclosed by the invention is characterized in that a matrix is quickly searched according to the coordinate offset of a pre-constructed pixel coordinate of a pre-distorted image relative to a pixel coordinate of a normal image, the pixel coordinate of the pre-distorted image corresponding to each pixel coordinate of the normal image in the normal image input by the AR-HUD system is calculated, the color information of the pixel point of the normal image is copied to the corresponding pixel point of the pre-distorted image to obtain an initial pre-distorted image, each hole point of the initial pre-distorted image relative to the normal image is subjected to color filling by adopting a neighborhood interpolation method to obtain the pre-distorted image, and the pre-distorted image is projected by the AR-HUD system to obtain a target image after imaging distortion correction is performed on the normal image. Because the method carries out distortion correction on the imaging of the AR-HUD system, the AR-HUD system can finally present a normal undistorted image on the front windshield of the automobile, thereby solving the problems in the prior art.
To further optimize the above embodiment, the imaging aberration correcting apparatus may further include: and a matrix construction unit.
Referring to fig. 7, a schematic structural diagram of a matrix building unit disclosed in the embodiment of the present invention, the matrix building unit may specifically include:
the acquiring subunit 401 is configured to acquire, by using a pre-established AR-HUD system model, coordinates of a normal image sampling point of the normal image sampling point in a physical coordinate system, and coordinates of a distorted image sampling point of the distorted image sampling point in the physical coordinate system, where the normal image sampling point corresponds to the distorted image sampling point;
in practical application, Zemax software can be used for establishing the AR-HUD system model, and the specific establishing process can refer to the existing scheme, which is not described herein again.
In this embodiment, can utilize AR-HUD system model to track light through the light trajectory of AR-HUD system, acquire the normal image sampling point coordinate of normal image sampling point under the physical coordinate system to and the distortion image sampling point coordinate of distortion image sampling point under the physical coordinate system.
The coordinates of each normal image sampling point in the normal image and the corresponding physical position of each distorted image sampling point in the distorted image in the physical coordinate system can be seen in fig. 3, the normal image sampling point is a sampling point located on the grid, and the distorted image sampling point is a sampling point located outside the grid.
A coordinate system transformation subunit 402, configured to transform each normal image sampling point coordinate from the physical coordinate system to the pixel coordinate system to obtain a corresponding normal image pixel coordinate, and transform each distorted image sampling point coordinate from the physical coordinate system to the pixel coordinate system to obtain a corresponding distorted image pixel coordinate;
and the calculating subunit 403 is configured to calculate a coordinate offset of the distorted image sampling point relative to the normal image sampling point based on the distorted image pixel point coordinates and the corresponding normal image pixel point coordinates, so as to obtain a coordinate offset fast lookup matrix.
The calculating subunit 403 is specifically configured to:
based on the distribution rule of pixel point coordinates of each normal image, partitioning the normal image by using a grid method to obtain a plurality of sub-images;
respectively solving the coordinate offset of a non-sampling point in each sub-image according to the coordinate offset of a normal image sampling point in each sub-image;
and constructing a coordinate offset fast search matrix of the pre-distortion image relative to the normal image according to the coordinate offset of the normal image sampling points corresponding to all the sub-images in the normal image and the coordinate offset of the non-sampling points.
To further optimize the above embodiment, the matrix building unit may further include: an offset amount operator unit;
the offset amount operator unit is specifically configured to:
when the number of the normal image sampling points is 16, the process of obtaining the offset of the non-sampling point P ═ D (i + dx, j + dy) according to the coordinate offset of the 16 normal image sampling points includes:
the 16 normal image sampling points are respectively:
in the formula, xiAnd yjRespectively the abscissa and ordinate of the sampling point closest to the non-sampling point, i is the row number, and j is the column number;
solving a coordinate offset matrix X in the X direction and a coordinate offset matrix Y in the Y direction between 16 normal image sampling points and the corresponding pre-distorted image sampling points;
the coordinate offset quantity Deltadx in the X direction and the coordinate offset quantity Deltady in the Y direction of the non-sampling point P ═ D (i + dx, j + dy) are obtained according to the coordinate offset matrix X and the coordinate offset matrix Y.
The X-direction coordinate offset matrix X corresponding to each normal image sampling point is as follows:
then, the coordinate offset Δ dx of the non-sampling point P ═ D (i + dx, j + dy) in the X direction can be obtained from the coordinate offset matrix X as follows:
where dx is the distance of point D (i + dx, j + dy) in the X direction relative to point D (i, j), dy is the distance of point D (i + dx, j + dy) in the y direction relative to point D (i, j), the intermediate matrix is the X-direction offset matrix X of the 16 normal image sample points adjacent to D (i + dx, j + dy), s (X) is the BiCubic basis function, which is a cubic function approximating the theoretical optimal interpolation function sin (pi X)/X, and the expression of s (X) is as follows:
in the formula, the optimal value of a is-0.5;
the coordinate offset Δ dy in the y direction of the non-sampling point P ═ D (i + dx, j + dy) is calculated as follows:
the coordinate offset matrix Y in the Y direction corresponding to each normal image sampling point is:
then, the coordinate offset Δ dy of the non-sampling point P ═ D (i + dx, j + dy) in the Y direction can be obtained from the coordinate offset matrix Y as follows:
where dx is the distance of point D (i + dx, j + dy) in the x direction relative to point D (i, j), dy is the distance of point D (i + dx, j + dy) in the Y direction relative to point D (i, j), the intermediate matrix is the Y-direction offset matrix Y of the 16 normal-image sample points to which D (i + dx, j + dy) is adjacent, and s (x) is the BiCubic basis function, which is a cubic function approximating the theoretical optimal interpolation function sin (pi x)/x, the expression of s (x) is as follows:
in the formula, the optimal value of a is-0.5.
In summary, the imaging distortion correction device of the AR-HUD system disclosed by the invention is characterized in that a matrix is quickly searched according to the coordinate offset of a pre-constructed pixel coordinate of a pre-distorted image relative to a pixel coordinate of a normal image, the pixel coordinate of the pre-distorted image corresponding to each pixel coordinate of the normal image in the normal image input by the AR-HUD system is calculated, the color information of the pixel point of the normal image is copied to the corresponding pixel point of the pre-distorted image to obtain an initial pre-distorted image, each hole point of the initial pre-distorted image relative to the normal image is subjected to color filling by adopting a neighborhood interpolation method to obtain the pre-distorted image, and the pre-distorted image is projected by the AR-HUD system to obtain a target image after imaging distortion correction is performed on the normal image. Because the method carries out distortion correction on the imaging of the AR-HUD system, the AR-HUD system can finally present a normal undistorted image on the front windshield of the automobile, thereby solving the problems in the prior art.
In addition, the method for constructing the coordinate offset of the pre-distortion image relative to the normal image to quickly search the matrix can reduce the real-time influence and data calculation amount of an operation algorithm on an AR-HUD system. And the correction accuracy is improved by calculating the coordinate offset of the distorted image sampling point relative to the normal image sampling point.
Further, compared with the distortion correction method of the camera in the existing scheme, the method does not need to obtain the camera parameters to carry out distortion correction, and can realize the distortion correction only through simple calibration, so that the asymmetric distortion can be corrected well.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. An imaging aberration correction method for an AR-HUD system, comprising:
quickly searching a matrix according to the coordinate offset of the pre-constructed pixel point coordinate of the pre-distorted image relative to the pixel point coordinate of the normal image, and calculating the pixel point coordinate of the pre-distorted image corresponding to the pixel point coordinate of each normal image in the normal image input by the AR-HUD system;
copying the color information of the normal image pixel points to the corresponding pre-distortion image pixel points to obtain an initial pre-distortion image;
carrying out color filling on each hole point existing in the initial pre-distortion image by adopting a neighborhood interpolation method to obtain a pre-distortion image;
and projecting the pre-distortion image through the AR-HUD system to obtain a target image after the normal image is subjected to imaging distortion correction.
2. The imaging distortion correction method of claim 1, wherein the copying of the color information of the normal image pixel points to the corresponding pre-distorted image pixel points to obtain an initial pre-distorted image specifically comprises:
when the pixel point coordinates of the pre-distorted image are non-integers, the non-integer pixel point coordinates of the pre-distorted image are converted into integer pixel point coordinates of the pre-distorted image by adopting a nearest neighbor interpolation method; quickly searching a matrix according to the coordinate offset to obtain color information of a normal image pixel point corresponding to the integer pre-distorted image pixel point and copying the color information to the non-integer pre-distorted image pixel point;
and when the coordinates of the pixel points of the pre-distorted image are integers, directly copying the color information of the pixel points of the normal image corresponding to the coordinates of the pixel points of the pre-distorted image of the integers in the normal image to the pixel points of the pre-distorted image of the integers.
3. The imaging aberration correction method according to claim 1, wherein the coordinate offset fast-lookup matrix is constructed by the following process:
acquiring normal image sampling point coordinates of a normal image sampling point in a physical coordinate system and distorted image sampling point coordinates of a distorted image sampling point in the physical coordinate system by using a pre-established AR-HUD system model, wherein the normal image sampling point corresponds to the distorted image sampling point;
transforming each normal image sampling point coordinate from a physical coordinate system to a pixel coordinate system to obtain a corresponding normal image pixel point coordinate, and transforming each distorted image sampling point coordinate from the physical coordinate system to the pixel coordinate system to obtain a corresponding distorted image pixel point coordinate;
and calculating the coordinate offset of the distorted image sampling point relative to the normal image sampling point based on the distorted image pixel point coordinates and the corresponding normal image pixel point coordinates to obtain the coordinate offset fast search matrix.
4. The imaging distortion correction method according to claim 3, wherein the calculating a coordinate offset of a distorted image sampling point relative to a normal image sampling point based on the distorted image pixel point coordinates and the corresponding normal image pixel point coordinates to obtain the coordinate offset fast lookup matrix specifically comprises:
based on the distribution rule of pixel point coordinates of each normal image, partitioning the normal image by using a grid method to obtain a plurality of sub-images;
respectively solving the coordinate offset of a non-sampling point in each sub-image according to the coordinate offset of a normal image sampling point in each sub-image;
and according to the coordinate offset of the normal image sampling points corresponding to all the sub-images in the normal image and the coordinate offset of the non-sampling points, constructing a coordinate offset fast search matrix of the pre-distorted image relative to the normal image.
5. The imaging distortion correction method according to claim 3, wherein when the number of the normal image sampling points is 16, the process of obtaining the offset of the non-sampling point P ═ D (i + dx, j + dy) from the coordinate offsets of the 16 normal image sampling points comprises:
the 16 normal image sampling points are respectively:
in the formula, xiAnd yjRespectively the abscissa and ordinate of the sampling point closest to the non-sampling point, i is the row number, and j is the column number;
solving a coordinate offset matrix X in the X direction and a coordinate offset matrix Y in the Y direction between the 16 normal image sampling points and the corresponding pre-distorted image sampling points;
the coordinate offset quantity Deltadx in the X direction and the coordinate offset quantity Deltady in the Y direction of the non-sampling point P ═ D (i + dx, j + dy) are obtained according to the coordinate offset matrix X and the coordinate offset matrix Y.
6. An imaging aberration correcting device of an AR-HUD system, comprising:
the calculation unit is used for quickly searching a matrix according to the coordinate offset of the pixel point coordinates of the pre-distorted image relative to the pixel point coordinates of the normal image, and calculating the pixel point coordinates of the pre-distorted image corresponding to the pixel point coordinates of each normal image in the normal image input by the AR-HUD system;
the information copying unit is used for copying the color information of the normal image pixel points to the corresponding pre-distorted image pixel points to obtain an initial pre-distorted image;
the color filling unit is used for performing color filling on each hole point existing in the initial pre-distorted image by adopting a neighborhood interpolation method to obtain a pre-distorted image;
and the projection unit is used for projecting the pre-distortion image through the AR-HUD system to obtain a target image after the normal image is subjected to imaging distortion correction.
7. The imaging aberration correcting apparatus according to claim 6, wherein the information copying unit specifically includes:
the first information replication sub-unit is used for converting the non-integer pixel coordinates of the pre-distorted image into integer pixel coordinates of the pre-distorted image by adopting a nearest neighbor interpolation method when the pixel coordinates of the pre-distorted image are non-integers; quickly searching a matrix according to the coordinate offset to obtain color information of a normal image pixel point corresponding to the integer pre-distorted image pixel point and copying the color information to the non-integer pre-distorted image pixel point;
and the second information replication sub-unit is used for directly replicating the color information of the normal image pixel point corresponding to the integer predistortion image pixel point coordinate in the normal image to the integer predistortion image pixel point when the predistortion image pixel point coordinate is an integer.
8. The imaging aberration correcting device of claim 6, further comprising: a matrix construction unit;
the matrix construction unit specifically includes:
the acquiring subunit is used for acquiring the coordinates of a normal image sampling point in a physical coordinate system and the coordinates of a distorted image sampling point in the physical coordinate system by using a pre-established AR-HUD system model, wherein the normal image sampling point corresponds to the distorted image sampling point;
the coordinate system transformation subunit is used for transforming the coordinates of each normal image sampling point to a pixel coordinate system from a physical coordinate system to obtain corresponding coordinates of a normal image pixel point, and transforming the coordinates of each distorted image sampling point to the pixel coordinate system from the physical coordinate system to obtain corresponding coordinates of a distorted image pixel point;
and the calculation subunit is used for calculating the coordinate offset of the distorted image sampling point relative to the normal image sampling point based on the distorted image pixel point coordinates and the corresponding normal image pixel point coordinates, so as to obtain the coordinate offset fast search matrix.
9. The imaging aberration correction device of claim 8, wherein the computing subunit is specifically configured to:
based on the distribution rule of pixel point coordinates of each normal image, partitioning the normal image by using a grid method to obtain a plurality of sub-images;
respectively solving the coordinate offset of a non-sampling point in each sub-image according to the coordinate offset of a normal image sampling point in each sub-image;
and according to the coordinate offset of the normal image sampling points corresponding to all the sub-images in the normal image and the coordinate offset of the non-sampling points, constructing a coordinate offset fast search matrix of the pre-distorted image relative to the normal image.
10. The imaging aberration correcting device of claim 8, wherein the matrix construction unit further comprises: an offset amount operator unit;
the offset amount operator unit is specifically configured to:
when the number of the normal image sampling points is 16, the offset of the non-sampling point P ═ D (i + dx, j + dy) is obtained according to the coordinate offset of the 16 normal image sampling points, and the method comprises the following steps:
the 16 normal image sampling points are respectively:
in the formula, xiAnd yjRespectively the abscissa and ordinate of the sampling point closest to the non-sampling point, i is the row number, and j is the column number;
solving a coordinate offset matrix X in the X direction and a coordinate offset matrix Y in the Y direction between the 16 normal image sampling points and the corresponding pre-distorted image sampling points;
the coordinate offset quantity Deltadx in the X direction and the coordinate offset quantity Deltady in the Y direction of the non-sampling point P ═ D (i + dx, j + dy) are obtained according to the coordinate offset matrix X and the coordinate offset matrix Y.
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