CN114881874B - High-resolution image generation method based on adaptive optical telescope imaging process - Google Patents
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Abstract
The invention discloses a high-resolution image generation method based on an imaging process of an adaptive optical telescope, which comprises the following steps: inputting the published or measured target parameters into MAYA software to generate a target three-dimensional model, and projecting the three-dimensional model in different directions under different illumination angles to obtain a simulation image; and performing motion blur degradation on the simulated image at different angles and different amplitudes to obtain an image after the motion blur degradation, obtaining an optical transfer function of an imaging system according to information such as parameters (wave band, caliber and focal length) of the adaptive optical telescope system, performing turbulence aberration degradation at different degrees on the image after the motion blur degradation, and finally considering the noise influence in the imaging process to obtain a final degraded image. The generation method considers the main factors causing the image quality deterioration of the adaptive optics high-resolution diagram, and provides a data basis for improving the performance of the adaptive optics system and restoring the adaptive optics high-resolution diagram image.
Description
Technical Field
The invention belongs to the field of adaptive optics and computer simulation, and particularly relates to a high-resolution image generation method based on an adaptive optics telescope imaging process.
Background
At present, a plurality of sets of self-adaptive optical foundation photoelectric telescopes are established in China, and the self-adaptive optical foundation photoelectric telescopes have the capability of acquiring large-batch and multi-batch high-resolution observation target images. However, in actual observation, the observation target is blurred due to the influence of various factors, the imaging result is degraded, and meanwhile, the adaptive optical system cannot completely correct the system error, so that the subsequent information mining is influenced.
The optical image degradation is caused by a plurality of reasons, and the optical image degradation mainly comprises an optical telescope system and the atmosphere. From the perspective of a telescope system, the aperture size of the telescope, tracking jitter, system errors, low contrast between a target and a background, unreasonable exposure parameter setting, increased sensor noise, image transmission loss and the like all cause the reduction of imaging quality, and the target is blurred. Atmospheric turbulence is the random fluctuation of the refractive index of the atmosphere caused by random movement of the atmosphere, caused by random variations in temperature, humidity and velocity fields. When light is transmitted in atmospheric turbulence, it is necessarily affected by the disturbance of the atmospheric turbulence. This has a serious effect on the observation of the target, which reduces the detectability of the target, resulting in a reduction in the resolution of the target.
Although the complex axis tracking system and the adaptive optics system can largely correct the above-mentioned errors, the adaptive optics still has insufficient compensation or correction of the imaging quality due to the influence of the system errors of the adaptive optics system itself, noise, and the like. Therefore, the method has great significance for further analyzing the image acquired by the adaptive optics system, constructing an adaptive optics high-resolution image degradation model, optimizing the relevant parameters of the adaptive optics system and further restoring the information value of the observation image mining observation image.
The conventional degradation method at present only considers the influence of a single factor such as noise, atmospheric turbulence and the like on the imaging result. The method is based on the imaging process of the self-adaptive optical ground-based telescope, and comprehensively considers the influence factors such as target attitude, illumination conditions, motion blur, telescope system parameters, atmospheric turbulence, noise and the like, provides a high-resolution image generation method based on the imaging process of the self-adaptive optical ground-based telescope, and provides an important data base for improving the performance of the self-adaptive optical system and enhancing and restoring observation images.
Disclosure of Invention
The technical problem solved by the invention is as follows: in order to solve the problem that the traditional degradation method only considers the influence of single factors such as noise, atmospheric turbulence and the like on an imaging result, the invention provides a high-resolution image generation method based on the imaging process of an adaptive optics foundation telescope by comprehensively considering the influence factors such as target attitude, illumination conditions, motion blur, telescope system parameters, atmospheric turbulence, noise and the like.
The technical scheme adopted by the invention is as follows: a high-resolution image generation method based on an adaptive optical telescope imaging process utilizes modules including a simulation image generation module, a motion blur degradation module, an atmospheric turbulence degradation module and a noise degradation module. The simulated image is degraded through motion blur, atmospheric turbulence and noise of different degrees to obtain a degraded image. The simulation image generation module is mainly used for constructing an observation target three-dimensional model by using known observation target parameters, and projecting the three-dimensional model to different view angle directions by using a Schneider projection method in consideration of the randomness of the posture change of the observation target to obtain different two-dimensional images. And (3) when the attitude change of the observation target is simulated, considering the influence of the illumination angle and the illumination intensity, and inputting the projection direction, the illumination angle and the illumination intensity as a model to obtain a simulated image.
Image＝IDEAL(θ,α,l)； (1)
The Image in the formula (1) is a generated simulation Image, theta represents different postures of the target in the projection direction, alpha is the illumination direction, l is the illumination intensity, and IDEAL (theta, alpha, l) is the process of traversing different postures, and obtaining a two-dimensional projection Image in the illumination direction and the illumination intensity. By summarizing actual observation data, motion jitter caused by frame tracking has a large influence on a final imaging result. The motion blur degradation module considers the influence of the motion shake direction and shake intensity on the imaging result.
Blurred s ＝IFFT(FFT(Image)×FFT(S(l,t))) (2)
S (l, t) in the formula (2) represents a time domain kernel of motion blur, the degradation degree of the motion blur is changed by changing l motion blur scale and t motion blur angle, fourier transform multiplication is carried out on the input image after Fourier transform, and the degraded image Blurred of the motion blur is obtained after inverse Fourier transform s . The observation imaging of the foundation large-aperture optical telescope is mainly influenced by atmospheric turbulence, modeling is carried out aiming at atmospheric disturbance, and the observation imaging process is simulated.
Blurred t ＝IFFT(FFT(Blurred s )×FFT(|FFT{P(u,v)}| 2 )) (3)
In the formula (3), P (u, v) is a pupil function, represents telescope parameter information and wavefront information, performs Fourier transform on the telescope parameter information and the wavefront information to obtain a Point Spread Function (PSF), performs Fourier transform on the point spread function to obtain an Optical Transfer Function (OTF), performs Fourier transform on an input image and multiplies the result of the optical transfer function by the Fourier transform to obtain a result, performs inverse Fourier transform on the result, and returns the result to a time domain to obtain a turbulently degraded image Blurred t . The degree of turbulence degradation is changed by changing the wavefront information in the above process. The noise degradation module simulates image degradation caused by detector factors in an image imaging process.
In the formula (4) I N Is a noise degraded image. I (x, y) is the pixel value of the image to be degraded; max (I (x, y)) is the maximum value of the image pixel to be degraded; (x, y) is an image pixel coordinate index; n is Gaussian noise with the same size as the image, k is an adjusting coefficient, and images with different peak signal-to-noise ratios are generated by adjusting the value of k.
The principle of the invention is as follows: the high-resolution self-adaptive optical observation image for observing the target is obtained through a large-aperture photoelectric telescope, and the imaging process of the high-resolution self-adaptive optical observation image is greatly influenced by target attitude, illumination conditions, motion blur, telescope system parameters, atmospheric turbulence, noise and the like, different from the conventional camera image.
Compared with the prior art, the invention has the following advantages: a high-resolution image generation method based on an adaptive optical telescope imaging process simulates a distortion process of high-resolution image imaging and solves the problem of single characteristic of a traditional simulation method. An important data base is provided for improving the performance of the adaptive optical system and enhancing and restoring the observation image; and data set support is provided for establishing a uniform adaptive optical image quality evaluation system.
Drawings
FIG. 1 is a flow chart of a method for generating high resolution images based on an adaptive optical telescope imaging process according to the present invention;
FIG. 2 is a simulated image with different postures and different illumination;
FIG. 3 is a diagram of a motion blur kernel with different scales and 45 degrees angles;
FIG. 4 is an image of different degradation levels under the same pose and lighting conditions;
FIG. 5 is an example of an optically degraded image of the present invention;
fig. 6 is a schematic diagram of an image degraded by the method and an image actually acquired by the optical system, wherein fig. 6 (a) is the image degraded by the method, and fig. 6 (b) is the image actually acquired by the optical system.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
As shown in figure 1, the invention discloses a high-resolution image generation method based on an adaptive optical telescope imaging process, which consists of a simulation image generation module 1, a motion blur degradation module 2, an atmospheric turbulence degradation module 3 and a noise degradation module 4, wherein:
the simulated image is degraded through motion blur, atmospheric turbulence and noise of different degrees to obtain a degraded image. The simulation image generation module 1 mainly uses known observation target parameters to construct an observation target three-dimensional model, and projects the three-dimensional model to different view angle directions by using a Schneider projection method in consideration of the randomness of the posture change of the observation target to obtain different two-dimensional images. And (3) when the attitude change of the observation target is simulated, considering the influence of the illumination angle and the illumination intensity, and inputting the projection direction, the illumination angle and the illumination intensity as a model to obtain a simulation image.
Image＝IDEAL(θ,α,l)； (1)
The Image in the formula (1) is a generated simulation Image, theta represents different postures of the target in the projection direction, alpha is the illumination direction, l is the illumination intensity, and IDEAL (theta, alpha, l) is the process of traversing different postures, and obtaining a two-dimensional projection Image in the illumination direction and the illumination intensity. By summarizing actual observation data, motion jitter caused by frame tracking has a large influence on a final imaging result. The motion blur degradation module 2 takes into account the influence of the motion shake direction and shake intensity on the imaging result,
Blurred s ＝IFFT(FFT(Image)×FFT(S(l,t))) (2)
s (l, t) in the formula (2) represents a time domain kernel of motion blur, the degradation degree of the motion blur is changed by changing l motion blur scale and t motion blur angle, fourier transform multiplication is carried out on the input image after Fourier transform, and the degraded image Blurred of the motion blur is obtained after inverse Fourier transform s . The observation imaging of the foundation large-aperture optical telescope is mainly influenced by atmospheric turbulence, modeling is carried out aiming at atmospheric disturbance, the atmospheric turbulence degradation module 3 simulates the observation imaging process,
Blurred t ＝IFFT(FFT(Blurred s )×FFT(|FFT{P(u,v)}| 2 )) (3)
in the formula (3), P (u, v) is a pupil function, represents telescope parameter information and wavefront information, performs Fourier transform on the telescope parameter information and the wavefront information to obtain a Point Spread Function (PSF), performs Fourier transform on the point spread function to obtain an Optical Transfer Function (OTF), performs Fourier transform on an input image and multiplies the result of the optical transfer function by the Fourier transform to obtain a result, performs inverse Fourier transform on the result, and returns the result to a time domain to obtain a turbulently degraded image Blurred t . The degree of turbulence degradation is changed by changing the wavefront information in the above process. The noise degradation module 4 simulates the image degradation caused by detector factors during image imaging,
in the formula (4) I N Is a noise degraded image. I (x, y) is the pixel value of the image to be degraded; max (I (x, y)) is the maximum value of the image pixel to be degraded; (x, y) is an image pixel coordinate index; n is Gaussian noise with the same size as the image, k is an adjusting coefficient, and the image with different peak signal-to-noise ratios is generated by adjusting the value of k.
The high-resolution image generation method in the imaging process of the adaptive optical telescope comprises the following steps: and constructing an observation target three-dimensional model by using known observation target parameters, and projecting the three-dimensional model to different view angle directions by using projection methods such as Schneider projection and the like in consideration of the randomness of the posture change of the observation target to obtain different two-dimensional images. When the change of the target posture is simulated and observed, the influence of the illumination angle and the illumination intensity is considered, the projection direction, the illumination angle and the illumination intensity are used as model input to obtain a simulation image as shown in fig. 2, the left graph in fig. 2 is a target posture euler angle (0, 0), the simulation image obtained by rendering is assumed that the position of the target is the coordinate origin (same below) and the illumination direction is (-1, 0), the right graph in fig. 2 is a target posture euler angle (0, 45, 0), and the simulation image obtained by rendering is assumed that the position of the target is the coordinate origin (same below) and the illumination direction is (1, 1).
Summarizing the actual observation data, it was found that the imaging results were severely degraded by motion blur, atmospheric turbulence and system noise. The simulation images obtained in the above process are convolved with motion blur kernels with different scales and different directions to simulate degraded images with different motion blur degrees, and as shown in fig. 3, the degraded images are motion blur kernels with different sizes and angles of 45 degrees (as shown in fig. 3, the sizes of the upper left graph, the upper right graph, the lower left graph and the lower right graph are 5 × 5,9 × 9, 13 × 13 and 17 × 17, respectively).
Random wavefronts are generated through Zernike polynomials, and standard deviations of the random wavefronts are changed for simulating different degrees of atmospheric turbulence degradation. And (3) calculating and generating a point spread function by using the random wavefront and telescope parameters, wherein the calculation method is shown as a formula (5), and simulating a degraded image after atmospheric turbulence degradation by using a simulated image convolution point spread function generated in the process.
P(u,v)＝A(u,v)exp(jφ(u,v)) (5)
And adjusting the signal-to-noise ratio of the image by adjusting K in the formula (4), and simulating the influence of noise of different degrees on the image quality, thereby simulating the image degradation caused by detector factors in the image imaging process. As shown in fig. 4, images with different degradation degrees under the same posture and lighting conditions (fig. 4 shows a simulated image in row 1, a degradation degree of 1 in row 2 (root mean square of the surface shape of the wave surface is 1, the same below), a degradation degree of 3 in row 3, a simulated image in row 2, a degradation degree of 1 in row 2, and a degradation degree of 3 in row 3).
The experimental results of the present invention are shown in fig. 5, and the present invention can simulate adaptive optics images with different blur degrees (fig. 5, 1 st row, 2 nd row, 3 rd row, 4 th row, 1 st row, 2 nd row, 3 rd row, 4 th row, etc. are examples of effects, and no special description is given).
Finally, the image actually acquired by the adaptive optics system (fig. 6 (b)) is compared with the image degraded by the method (fig. 6 (a)).
In a word, the invention constructs a high-resolution image generation method based on the imaging process of the adaptive optical telescope; a high-resolution image generation method based on an adaptive optical telescope imaging process simulates a distortion process of high-resolution image imaging and solves the problem of single characteristic of a traditional simulation method. And an important data base is provided for improving the performance of the adaptive optics system and enhancing and restoring the observation image.
The invention has not been described in detail and is part of the common general knowledge of a person skilled in the art.
Claims (2)
1. A high-resolution image generation method based on an adaptive optical telescope imaging process is characterized by comprising the following steps: the method utilizes modules comprising: the simulation image generation module (1), the motion blur degradation module (2), the atmospheric turbulence degradation module (3) and the noise degradation module (4), wherein the simulation image is degraded through motion blur of different degrees, the atmospheric turbulence degradation and the noise degradation to obtain a degraded image, and the method specifically comprises the following processes:
the simulation image generation module (1) constructs a three-dimensional model of an observation target by using known parameters of the observation target, projects the three-dimensional model to different visual angle directions by using a Schneider projection method in consideration of the randomness of the posture change of the observation target to obtain different two-dimensional images, increases the influence of an illumination angle and an illumination intensity while simulating the posture change of the observation target, and obtains a simulation image by using the projection direction, the illumination angle and the illumination intensity as model inputs,
Image＝IDEAL(θ，α，l)； (1)
in the formula (1), image is a generated simulation Image, theta represents different postures of a target in a projection direction, alpha is an illumination direction, l is illumination intensity, and IDEAL (theta, alpha, l) is a process of traversing different postures, and acquiring a two-dimensional projection Image in the illumination direction and the illumination intensity;
the motion blur degradation module (2) considers the influence of the motion shake direction and shake intensity on the imaging result,
Blurred s ＝IFFT(FFT(Image)×FFT(S(l，t))) (2)
s (l, t) in the formula (2) represents a time domain kernel of motion blur, the degradation degree of the motion blur is changed by changing l motion blur scale and t motion blur angle, fourier transform multiplication is carried out on the input image after Fourier transform, and the degraded image Blurred of the motion blur is obtained after inverse Fourier transform s ，
The observation imaging of the foundation large-aperture optical telescope is mainly influenced by atmospheric turbulence, modeling is carried out aiming at atmospheric disturbance, an atmospheric turbulence degradation module (3) simulates the observation imaging process,
Blurred t ＝IFFT(FFT(Blurred s )×FFT(|FFT{P(u，v)}| 2 )) (3)
in the formula (3), P (u, v) is a pupil function, represents telescope parameter information and wavefront information, performs Fourier transform on the telescope parameter information and the wavefront information to obtain a Point Spread Function (PSF), performs Fourier transform on the point spread function to obtain an Optical Transfer Function (OTF), performs Fourier transform on an input image and multiplies the result of the optical transfer function by the Fourier transform to obtain a result, performs inverse Fourier transform on the result, and returns the result to a time domain to obtain a turbulently degraded image Blurred t Wherein the degree of turbulence degradation is varied by varying the wavefront information;
the noise degradation module (4) simulates image degradation caused by detector factors in the image imaging process,
in the formula (4) I N For the noise degraded image, I (x, y) is the pixel value of the image to be degraded; max (I (x, y)) is the maximum value of the image pixel to be degraded; (x, y) is an image pixel coordinate index; n is the gaussian noise of the same size as the image, k is the adjustment coefficient,images with different peak signal-to-noise ratios are generated by adjusting the k value.
2. The method of claim 1, wherein the method further comprises: the method comprehensively considers the target attitude, the illumination condition, the motion blur, the telescope system parameters, the atmospheric turbulence and the influence caused by noise in the imaging process to generate the degraded image.
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