CN108198131B - Spatial target shaking and motion blur simulation method based on multi-image fusion - Google Patents
Spatial target shaking and motion blur simulation method based on multi-image fusion Download PDFInfo
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Abstract
The invention discloses a space target shaking and motion blur simulation algorithm based on multi-image fusion, which comprises the following steps: s1: acquiring motion curves of offset and time of a space target relative to the center of an image in the directions of an x axis and a y axis; s2: sampling motion curves of a space target in the directions of an x axis and a y axis; s3: generating a clear simulation image for each sampling point by utilizing an OpenGL programming interface; s4: superposing a plurality of clear simulation images with spatial targets generated in the step S3 on a rear buffer area of an OpenGL programming interface frame by frame, and fusing a plurality of frame images in a pixel-by-pixel averaging mode; s5: and obtaining the jitter and motion blur simulation image of the space target. According to the method, the spatial target motion curve is sampled to generate multi-frame clear simulation images under different motion quantity conditions, and then fusion is performed by utilizing a multi-image averaging mode, so that a spatial target motion fuzzy simulation image is generated, and a spatial target real image can be better simulated.
Description
Technical Field
The invention relates to the technical field of digital image processing, in particular to a motion blur image simulation technology.
Background
At present, a spatial target image processing technology is a key part of a spatial target monitoring system, is a basis for entering a space, knowing the space and controlling the space, and is also an indispensable link for space attack and defense. Generally, spatial target image processing techniques are based on simulated images.
However, the shake of the space target simulation image is usually caused by the tiny vibration of the platform camera, and the motion blur of the space target simulation image is caused by the relative motion between the platform and the target satellite, and the blur has a certain influence on the precision of the image processing process and needs to be processed. The existing spatial target shaking and motion blur image simulation technology is generally carried out by adopting a mode of constructing a blur kernel, the blur kernel with specific properties is constructed according to the motion characteristics of a spatial target, and then the clear spatial target simulation image is subjected to convolution operation by using the blur kernel, so that the spatial target shaking and motion blur simulation image under specific shaking and motion characteristics is obtained; the fuzzy image simulation technology of the space target motion based on the fuzzy core has certain limitation, which is mainly reflected in that the existing algorithm is only suitable for simple motion conditions and can only simulate the shake or relative motion in a certain single direction, and the motion conditions of the space target in the actual conditions are very complex and cannot be simulated by simple linear motion. Therefore, the characteristics of the spatial target motion blurred image cannot be truly represented by the existing spatial target motion blurred image simulation technology based on the constructed blur kernel, and the research of an image processing technology aiming at the spatial target motion blurred image is difficult to support.
Therefore, how to provide a simulation algorithm for improving a motion-blurred image of a spatial object is a problem to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of this, the invention provides a spatial target dithering and motion blur simulation algorithm based on multi-image fusion, and compared with the prior art, the spatial target dithering and motion blur simulation algorithm has the advantages of being suitable for more complicated spatial target motion conditions and being capable of better simulating a real image of a spatial target.
In order to achieve the purpose, the invention adopts the following technical scheme:
a spatial target dithering and motion blurring simulation algorithm based on multi-image fusion comprises the following steps:
s1: acquiring motion curves of offset and time of a space target relative to the center of an image in the directions of an x axis and a y axis;
s2: photographing and sampling motion curves of the space target in the directions of an x axis and a y axis;
s3: generating a clear simulation image for each sampling point by utilizing an OpenGL programming interface;
s4: superposing a plurality of clear simulation images with spatial targets generated in the step S3 on a rear buffer area of an OpenGL programming interface frame by frame, and fusing a plurality of frame images in a pixel-by-pixel averaging mode;
s5: and obtaining the jitter and motion blur simulation image of the space target.
Further, step S3 specifically includes:
s31: building a three-dimensional model of a space target by utilizing OpenGL, and setting the material, position and orientation of the space target;
s32: setting the attribute, position and direction of the light source;
s33: setting the position and the pointing direction of the camera model;
s34: generating a simulation image through a perspective projection imaging model of OpenGL;
further, the fusion algorithm of step S4 adopts a pixel average fusion algorithm, and the specific calculation method is as follows:
the method comprises the steps of obtaining a fuzzy Image, obtaining a sampling point number, representing the number of lines and columns of the Image by N, representing the number of sampling points by i, j, representing the number of lines and columns of the Image, representing the clear Image at each moment simulated by OpenGL, representing the fuzzy Image obtained by pixel-by-pixel averaging by adopting an Image average fusion algorithm, smoothing the Image, improving the signal-to-noise ratio of the Image and improving the reliability of detection.
Further, the number of the sampling points in the step S2 is 20-30 points per second;
further, the number of sampling points in step S2 is 25 points per second.
The number of points collected every second determines the authenticity of the space target motion blur simulation image, the more the points are collected, the closer the obtained simulation image is to the real situation, the higher the requirement on hardware is, the larger the processed data volume is, and the optimal value is selected to be 25 sampling points within 1 second by comprehensively considering the real degree of the simulation image and the calculation cost.
Compared with the prior art, the invention provides a spatial target dithering and motion blur simulation algorithm based on multi-image fusion, which improves the simulation technology of the spatial target dithering and motion blur images, multi-frame clear simulation images under different motion quantity conditions are generated by sampling a spatial target motion curve, and then are fused in a multi-image averaging mode, thereby generating a space target motion fuzzy simulation image, being suitable for more complicated space target motion conditions, the method can better simulate the real image of the space target, realize the shake of the space target and the simulation of the relative motion blurred image under the complex motion condition, provide data closer to the real situation for the motion blurred image processing technology of the space target, and support the development of the related research of the motion blurred image processing technology of the space target.
Drawings
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 provided drawings without creative efforts.
FIG. 1 is a flow chart of the fuzzy image simulation algorithm of the present invention;
FIG. 2 is a flowchart illustrating operation of step S3 according to the present invention;
FIG. 3 is a drawing of a spatial target blur simulation image I of a spatial target dithering and motion blur simulation algorithm based on multi-image fusion according to the present invention;
FIG. 4 is a diagram of a second spatial target blur simulation image of a spatial target dithering and motion blur simulation algorithm based on multi-image fusion according to the present invention;
FIG. 5 is a schematic diagram of a spatial target blur simulation image III based on a spatial target dithering and motion blur simulation algorithm of multi-image fusion according to the present invention;
FIG. 6 is a diagram of a spatial target blur simulation image IV based on a spatial target dithering and motion blur simulation algorithm of multi-image fusion according to the present invention.
Detailed Description
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 and fig. 2, an embodiment of the present invention discloses a spatial target dithering and motion blur simulation algorithm based on multi-image fusion, which includes the following steps:
s1: acquiring motion curves of offset and time of a space target relative to the center of an image in the directions of an x axis and a y axis;
s2: photographing and sampling motion curves of the space target in the directions of an x axis and a y axis;
s3: generating a clear simulation image for each sampling point by utilizing an OpenGL programming interface;
s4: superposing a plurality of clear simulation images with spatial targets generated in the step S3 on a rear buffer area of an OpenGL programming interface frame by frame, and fusing a plurality of frame images in a pixel-by-pixel averaging mode;
s5: and obtaining the jitter and motion blur simulation image of the space target.
The algorithm realizes space target shaking and relative motion fuzzy image simulation under the condition of complex motion by a multi-image fusion mode and utilizing a back cache mode of OpenGL.
The above steps will be described in detail below.
In this embodiment, in step S1, motion curves of the offset of the spatial target in the x-axis direction and the y-axis direction relative to the image center and time are obtained, and the ideal state of the spatial target is that the motion curve of the spatial target is a sine curve;
in some embodiments, the algorithm can also utilize a measurement system consisting of a navigation satellite system and a distance sensor to acquire a space target motion curve in real time.
In step S2, photographing and sampling the motion curves of the spatial target in the x-axis and y-axis directions;
in this embodiment, in step S2, a CCD industrial camera may be used for photographing and imaging;
in some embodiments, since the moving range of the spatial target is wide, it is difficult to perform the measurement by one set of cameras, so that multiple sets of cameras can be arranged to perform the photographing in the actual sampling process.
In this embodiment, step S3 specifically includes the following steps:
s31: building a three-dimensional model of a space target by utilizing OpenGL, and setting the material, position and orientation of the space target;
in this embodiment, the spatial target three-dimensional model is an input of the algorithm, and the storage format of the three-dimensional model adopts a mated star obj file and a star mtl file, wherein the star obj file mainly stores vertex coordinates, normal vectors and patch structure information of the three-dimensional model, the vertex coordinates and the normal vectors are three-dimensional vectors, and the patch structure information includes three-dimensional vertex indexes constituting the patch; *. mtl file mainly stores material information, which is mainly the reaction of setting space target to light source, the material attribute in mtl file includes shadow color (Ka), solid color (Kd), high light color (Ks), corresponding data r, g, b; reflection high optical coefficient (Ns), filter transmittance (Tf), fade-out index (d), refractive index (Ni).
In this embodiment, coordinates and normal vectors of all vertices of the space target are read from an x.obj file of the three-dimensional model, meanwhile, material information is read from an x.mtl file of the three-dimensional model and is given to a corresponding triangular patch, the three-dimensional model is drawn in a triangular patch mode according to patch structure information provided in the x.obj file, and the three-dimensional space target is constructed by using triangles by using glVertex3f () and glNormal3f () functions.
In some embodiments, the normal vector may be set to (0,0,1), and the triangular patch coordinate information may be set to (-10.0, -5.0, -2.0), (-12.3, -7.5, -5.0), and (-8.5, -6.0, -4.0); in some embodiments, the shade color (Ka) may be (0.1,0.1,0.1), the solid color (Kd) may be (0.5,0.5,0), and the highlight color (Ks) may be (0.0,0.0, 0.0); the reflection high optical coefficient (Ns) may take a value of 18.0, the filter transmittance (Tf) may take a value of (1.0,1.0,1.0), the fade-out index (d) may take a value of 0.5, and the refractive index (Ni) may take a value of 1.00.
S32: setting the attribute, position and direction of the light source;
in this embodiment, the light source may be set to be white parallel light, the position is at (1,1,1) under the object coordinate system, the direction is directed to the origin of the object coordinate system, and the glLightfv () function may be used for setting.
In some embodiments, the light source may also be set to a yellow spotlight with position coordinates of (0.0,0.0, 1.0).
S33: setting the position and the pointing direction of the camera model;
in this embodiment, the camera model is set as the pinhole imaging model, the position is located under (0,0,30) the object coordinate system, the direction points to the origin of the object coordinate system element, and the glulookup () function can be used for setting.
In some embodiments, the camera is at the origin, pointing in the negative direction of the z-axis, and the upward vector is (0,1, 0).
S34: the simulated image is generated by a perspective projection imaging model of OpenGL,
in this embodiment, glfrustutum () function projection is used, and the generated simulation image is refreshed and displayed using glFlush () function.
In step S4, superimposing a plurality of clear simulation images with spatial targets frame by frame to a back buffer area of the OpenGL programming interface, and fusing the multi-frame images in a pixel-by-pixel averaging manner;
in this embodiment, a plurality of generated simulation images with clear spatial targets are superimposed frame by frame to a back buffer area of an OpenGL programming interface, a plurality of frame images are fused in a pixel-by-pixel averaging manner, and the generated simulation images are processed by using a pixel averaging fusion algorithm, where the specific principle is as follows:
wherein N is the number of samples, namely the data volume collected per second, and the value of N is 20; i, j represents the rows and columns of the Image, Image represents a clear Image at each moment simulated by OpenGL, and BlurImage represents a blurred Image obtained by pixel-by-pixel averaging to obtain a simulated Image of the shake and relative motion blur of the spatial target.
In some embodiments, the value of N may be determined according to the configuration of the actual computer hardware, and the specific range may be 20 to 30.
In step S5, a simulated image of the spatial target with shake and motion blur is obtained, and fig. 3-6 are motion blur images generated by the spatial target under different postures simulated by the present algorithm.
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. For the device disclosed by the embodiment, the description is relatively simple because the device corresponds to the algorithm disclosed by the embodiment, and the relevant points can be obtained by referring to the description of the algorithm part.
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 (4)
1. A space target shaking and motion blur simulation method based on multi-image fusion is characterized by comprising the following steps: the method comprises the following steps:
s1: acquiring motion curves of offset and time of a space target relative to the center of an image in the directions of an x axis and a y axis;
s2: photographing and sampling motion curves of the space target in the directions of an x axis and a y axis;
s3: generating a clear simulation image for each sampling point by utilizing an OpenGL programming interface;
step S3 specifically includes:
s31: building a three-dimensional model of a space target by utilizing OpenGL, and setting the material, position and orientation of the space target;
s32: setting the attribute, position and direction of the light source;
s33: setting the position and the pointing direction of the camera model;
s34: generating a simulation image through a perspective projection imaging model of OpenGL; s4: superposing a plurality of clear simulation images with spatial targets generated in the step S3 on a rear buffer area of an OpenGL programming interface frame by frame, and fusing a plurality of frame images in a pixel-by-pixel averaging mode;
s5: and obtaining the jitter and motion blur simulation image of the space target.
2. The method according to claim 1, wherein the method comprises the following steps: the fusion algorithm of step S4 adopts a pixel average fusion algorithm, and the specific calculation method is as follows:
where N is the number of sampling points, i, j represents the rows and columns of the Image, Image represents the sharp Image at each moment simulated by OpenGL, and blurrimage represents the blurred Image obtained by pixel-by-pixel averaging.
3. The method according to claim 1, wherein the method comprises the following steps: the number of sampling points in step S2 is 20-30 points per second.
4. The method according to claim 1, wherein the method comprises the following steps: the number of sampling points in step S2 is 25 points per second.
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