A method for exciting imaging of a space-based small satellite payload based on swivel-arm scanning

Abstract

The present invention discloses a space-based small satellite payload excitation imaging method based on rotating-arm scanning, comprising the steps of: 1) building a reflective Fourier ptychography imaging system based on rotating scanning; 2) using a CCD detector to perform rotating scanning on the Fourier plane of the imaging system to obtain an N-frame sequence of low-resolution images; 3) preprocessing the acquired original low-resolution image sequence; 4) respectively using the registration methods of correlation function and speckle fractal dimension to register the preprocessed image sequence; 5) using the Fourier ptychography imaging algorithm to reconstruct the registered image sequence to obtain a restored image with improved resolution. The present invention expands the spectral range by performing rotating scanning of the CCD detector in the far field, and uses the Fourier ptychography reconstruction algorithm to quickly reconstruct the amplitude information and phase information of the far-field target object, obtaining a restored image with a resolution exceeding the diffraction limit of the system objective lens, effectively improving the imaging quality of the macroscopic reflective imaging system.

Description

Technical Field

[0001] The present invention belongs to the field of space - based remote - sensing imaging, and particularly relates to a space - based small - satellite payload excitation imaging method based on spiral - arm scanning. Background Art

[0002] In recent years, with the rapid development of aerospace payload technology, space - based payload imaging technology has become a relatively mainstream technology for observing the ground and the air. It has the advantages of wide coverage, fast response speed, high reconnaissance efficiency, etc., and is widely used in fields such as resource exploration, meteorological observation, urban planning, national defense security, space exploration, etc., which is of great significance to national security and national economic construction. With the wide use of space - based payload detection and imaging systems in civilian and military fields, people have put forward higher requirements for their imaging quality. Therefore, how to effectively improve the quality of space - based payload detection images is the key issue for the further development of future space - based payload detection technology.

[0003] The imaging quality of a space - based payload imaging system is mainly affected by two aspects: the complex imaging environment and the optical imaging system. On the one hand, the imaging environment of a space - based payload imaging system is relatively complex. Adverse factors such as atmospheric disturbance, uneven illumination, and system tremor will bring additional noise to the imaging system, reducing the contrast of the space - based payload detection imaging system, resulting in low reliability, low information density, and low signal - to - noise ratio of space - based payload detection images. On the other hand, for a space - based payload imaging system, the light wave emitted from a certain point on the object surface presents a diffraction spot rather than a clear point on the image surface after long - distance propagation. Although the influence of diffraction blur can be reduced by increasing the aperture of the imaging system, using a large - aperture lens will increase the development cost of the system and extend the development cycle of the system, restricting its wide - range promotion and use. The above two aspects both impose certain limitations on the imaging effect of the space - based payload imaging system, reducing the quality of space - based payload detection images, affecting the interpretation, analysis, and use of space - based payload detection data, and restricting the wider application of space - based payload imaging systems. Therefore, how to perform image sharpening processing on degraded space - based payload detection images under limited hardware conditions to improve the contrast of space - based payload detection images and enhance the visual effect is an urgent problem to be solved in the field of space - based payload detection imaging.

[0004] In summary, due to the influence of the imaging environment and the optical system, the space - based payload imaging system has the problem of degradation of the quality of space - based payload detection images, which leads to poor imaging reliability and low accuracy of the space - based payload detection system, restricting the wider use of space - based payload detection images in various fields.

[0005] To address this need, a series of novel imaging systems and methods have been proposed in the field of computational imaging, aiming to obtain super-resolution images with a resolution higher than the Abbe limit, namely the so-called "super-resolution" imaging technology. The most representative one is Fourier Ptychographic Microscopy (FPM). This method illuminates the sample at multiple angles, records the low-resolution images at different illumination angles through a digital image receiver, and then uses a reconstruction algorithm to perform alternating iterations based on the mapping relationship between the spatial light intensity distribution information and the Fourier domain (i.e., frequency domain) angle information, finally obtaining the high-resolution intensity information and phase information of the sample, overcoming the restrictive relationship between the field of view and spatial resolution in traditional imaging systems and achieving super-resolution imaging.

[0006] In the past 20 years, many research teams have achieved a breakthrough in the resolution limit of traditional imaging systems based on Fourier ptychographic super-resolution imaging technology. In 2013, the Zheng team proposed a Fourier ptychographic imaging system based on multi-angle illumination by LEDs. This system uses an LED array to provide system illumination. Each LED bead is at a different position, so the incident angle when illuminating the sample is also different. One image is taken for each incident angle, and then a high-resolution image can be recovered from a series of low-resolution images using a reconstruction algorithm. In 2014, Williams et al. built a Fourier ptychographic imaging (FPM) tumor cell counting system based on an LED array. In the same year, Chung et al. built an automatic white blood cell counting system also based on Fourier ptychographic microscopy. In 2015, Horstmeyer et al. successfully achieved digital pathological analysis using a Fourier ptychographic imaging system based on an LED array. In 2016, Pacheco et al. first realized a reflective Fourier ptychographic imaging system and was able to apply it to the rapid large-field surface detection of samples.

[0007] All of the above super-resolution technologies based on Fourier ptychography have been studied from the perspective of microscopy, and the application of this technology in macroscopic imaging systems is not yet mature. Due to the essential differences between macroscopic imaging systems and microscopic imaging systems caused by the imaging distance, traditional Fourier ptychographic imaging technology is not suitable for long-distance imaging systems, and the method of using illumination angles for spectral scanning is not suitable for long-distance imaging systems. Therefore, when promoting this technology from the microscopic to the macroscopic, not only the model needs to be scaled up, but also the imaging system and reconstruction algorithm need to be modified and optimized based on the imaging principle of macroscopic reflective imaging systems. Summary of the Invention

[0008] The object of the present invention is to provide a space-based small satellite payload excitation imaging method based on swing-arm scanning. By combining rotational scanning image acquisition with a reconstruction algorithm, super-resolution imaging of a long-distance imaging system can be achieved, which is applicable to improving the resolution of a macroscopic reflection imaging system and target detection.

[0009] The technical solution for realizing the present invention is as follows: A space-based small satellite payload excitation imaging method based on swing-arm scanning, comprising the following steps:

[0010] Step 1, build a space-based small satellite payload synthetic aperture excitation imaging system based on swing-arm scanning, including a laser illumination module, a scanning imaging module, and a software control module. First, the pulsed light beam emitted by the laser passes through a beam expander lens to expand the beam, then a diaphragm is placed to control the beam quality of the expanded light to be as uniform as possible, and finally, the expanded light passes through a focusing lens to be focused into a parallel light beam, and then proceed to Step 2.

[0011] Step 2, control the rotating stage carrying the camera to rotate and scan on the Fourier plane of the imaging system, and collect an N-frame low-resolution image sequence under the original imaging lens aperture, where the imaging ranges of the CCD detectors overlap with an overlap rate of 80%, N≥24, and then proceed to Step 3.

[0012] Step 3, perform Gaussian filtering and image enhancement on the original images to make the useful high-frequency information in the images more obvious, obtain a preprocessed image sequence, and then proceed to Step 4.

[0013] Step 4, if the imaging target object is a smooth object, a registration method based on the transform domain is needed to register the preprocessed image sequence; if the target object is a rough object, a registration method based on the speckle fractal dimension is needed to register the preprocessed image sequence, obtain a registered image sequence, and then proceed to Step 5.

[0014] Step 5, use the Fourier ptychography reconstruction algorithm to reconstruct the registered images to obtain a restored image.

[0015] Compared with the prior art, the present invention has the following remarkable advantages: The present invention solves the problem of difficult illumination angle transformation when the traditional Fourier ptychography microscopy technology is extended to a macroscopic imaging system. It proposes to use the lens to scan on the spectrum plane of the imaging system to achieve equivalent spectrum scanning, designs a macroscopic Fourier ptychography imaging system based on rotational scanning, obtains a series of conventional images under the original imaging lens aperture, and uses the reconstruction algorithm to iterate the obtained images to expand the equivalent aperture of the imaging system, obtain a high-definition image with improved image quality, break through the diffraction limit of the macroscopic reflection imaging system without the premise of a high-cost large-aperture imaging lens, and reduce the cost of achieving super-resolution in the macroscopic reflection imaging system. Description of the Drawings

[0016] Figure 1 is a flowchart of a macroscopic reflection Fourier ptychography super-resolution imaging method based on rotational scanning.

[0017] Figure 2 is a system optical path diagram of a macroscopic reflection Fourier ptychography super-resolution imaging method based on rotational scanning.

[0018] Figure 3 is a schematic diagram of the light field propagation between the lens and the CCD detector.

[0019] Figure 4 is a schematic diagram of the rotational scanning path. Specific embodiments

[0020] Based on the traditional Fourier ptychography super-resolution microscopy technology, the present invention extends the Fourier ptychography super-resolution imaging technology from the microscopic scale to the macroscopic scale. According to the basic principle of Fourier ptychography, the macroscopic imaging system and spectral ptychography scanning are organically combined. It is proposed to use a CCD detector to perform rotational scanning on the Fourier plane of the Fourier ptychography imaging system to obtain a sequence of original low-resolution images, and use a reconstruction algorithm to iterate the obtained images to expand the equivalent aperture of the imaging system, obtain a restored image with improved resolution, and finally achieve super-resolution imaging of the macroscopic imaging system, thus expanding the application scope of the traditional Fourier ptychography super-resolution imaging technology.

[0021] The following will Figures 1 to 4 further elaborate in detail on a reflection Fourier ptychography super-resolution imaging method based on rotational scanning according to the present invention.

[0022] Combined with Figure 1 , the macroscopic Fourier ptychography super-resolution imaging method based on rotational scanning according to the present invention includes the following steps:

[0023] Step 1: Build a reflection Fourier ptychography imaging system based on rotational scanning. As Figure 2 shown, the Fourier ptychography imaging system based on rotational scanning includes a laser, a beam expander, a diaphragm, a convex lens, an object, an imaging lens at the reflection receiving end, a CCD detector, which are arranged coaxially in sequence, and also includes a precision electric rotary stage; wherein the imaging lens at the reflection receiving end is fixed on the CCD detector, and the imaging lens and the corresponding CCD detector are fixed on the precision electric rotary stage; the light emitted by the laser is expanded and filtered by the beam expander and the diaphragm to provide relatively uniform illumination, and then the convex lens converts the light field into parallel light to perform coherent illumination on the object, realizing far-field Fraunhofer diffraction at a finite distance; at this time, the light field distribution on the front surface of the imaging lens after the light field emitted by the object propagates over a long distance is the far-field Fraunhofer diffraction result of the object.

[0024] The light field emitted by the target object enters the imaging lens after long-distance propagation. The long-distance propagation is regarded as a Fraunhofer far-field propagation. After the Fourier spectrum is intercepted by the aperture of the imaging lens, it is then received by the CCD detector, and a sequence of N low-resolution original images under the aperture of the imaging lens is acquired. The above low-resolution images record the light intensity information of the target object. Taking the square root of the low-resolution images yields the amplitude information at the corresponding scanning positions.

[0025] Figure 3 Figure 4 is a schematic diagram of the light field propagation between the lens and the CCD detector. The illumination light field passes through the target object and is collected by the imaging lens and imaged on the target surface of the CCD detector. Among them, the imaging lens is equivalent to a Fourier transform, and the image obtained by the CCD detector is the intensity information of the target object in the far field.

[0026] Proceed to Step 2.

[0027] Step 2: Control the camera to perform rotational scanning on the Fourier plane of the imaging system, and acquire N low-resolution images as the original image sequence. Their imaging ranges overlap with an overlap rate of 80%. N≥24. Specifically as follows:

[0028] Step 2-1: Turn on the laser. The light emitted by the laser is expanded and filtered through a beam expander and a diaphragm to provide relatively uniform illumination. Then, it passes through a convex lens to convert the light field into a plane wave, which is irradiated on the target object. Adjust the power of the laser so that the illumination brightness is within an appropriate range to avoid overexposure or underexposure.

[0029] Step 2-2: Power on the CCD detector and set parameters such as the exposure time, refresh frequency, and gain of the CCD detector to ensure the quality of subsequent image acquisition.

[0030] Step 2-3: Use the host computer to send a signal to the precision electric rotary stage, so that the precision electric rotary stage drives the imaging lens and the CCD detector to perform rotational scanning as shown in Figure 4 to obtain N low-resolution images as the original image sequence

[0031] Proceed to Step 3.

[0032] Step 3: Perform distortion correction and image denoising on each image in the acquired original image sequence one by one to obtain a preprocessed image sequence:

[0033] Perform Gaussian smoothing processing at different scales:

[0034] L(x,y,σ) = G(x,y,σ) * I(x,y) (1)

[0035]

[0036] where σ is the Gaussian smoothing scale factor, L(x, y, σ) is the result of the smoothed image, G(x, y, σ) is the Gaussian smoothing operator with scale σ, I(x, y) is the intensity of the image to be processed, and * represents the convolution operation.

[0037] Through the above Gaussian smoothing process, a set of smoothed images is obtained, cropped into squares of 256 * 256 pixels, and the noise information at the image edges is removed, increasing the proportion of effective information in the image to reduce the computational complexity of the subsequent Fourier reconstruction algorithm, obtaining a preprocessed image sequence, and proceeding to step 4.

[0038] Step 4: Register the preprocessed image sequence to obtain a registered image sequence. Specifically, for the specular reflection target object image, use the method based on the transform domain to register the preprocessed image to obtain a registered image sequence; for the diffuse reflection target object, use the registration method based on the speckle fractal dimension to obtain a registered image sequence.

[0039] During the process of the camera scanning and collecting images in the preprocessed image sequence, the image of the object will shift on the detection surface. Conventional image registration algorithms are generally divided into registration methods based on gray level, features, and transform domain. The registration method based on gray level mainly uses the gray level information of the entire image to establish a similarity measure of the gray level information between the registered images and find the registration parameters corresponding to the maximum similarity. This method is suitable for images with linearly varying gray values. The feature-based image registration method mainly uses the gray level information of small regions of the image. Although this method requires a small amount of data, it is sensitive to the errors in feature extraction and matching. The registration method based on the transform domain is to transform the image from the spatial domain to the transform domain using a certain transformation relationship and then perform registration based on the information in the transform domain. This method is more suitable for registering bright-field and dark-field images when imaging smooth objects in an FP system. Therefore, we need to use the method of registering based on transform domain information to register bright-field and dark-field images of smooth objects. However, when the object sample is a rough object, the collected image is contaminated by strong speckle noise, and the difficulty of image registration increases at this time. Therefore, here we use the technique of in-plane displacement measurement of speckle images for image registration. This method is hereinafter referred to as the speckle image sub-pixel registration method.

[0040] During the image acquisition process of the camera scanning type FP system, as the camera moves, the image of the object will shift on the detection surface. Therefore, for the camera scanning type FP system, image registration must be performed first before the Fourier ptychography reconstruction algorithm. The specific operation is as follows:

[0041] Assume that the image I1(x, y) is translated by (Δx, Δy) to obtain the image I2(x, y), and the relationship is:

[0042] I2(x,y) = I1(x - Δx, y - Δy) (3)

[0043] Performing Fourier transforms on both sides of the above equation gives:

[0044] O2(u,v) = exp(-i2π(uΔx + vΔy))O1(u,v) (4)

[0045] Where O1(u,v) is the Fourier transform of I1(x,y), O2(u,v) is the Fourier transform of I2(x,y), i represents the imaginary number, and u and v are the horizontal and vertical coordinates of the frequency spectrum diagram after the image undergoes Fourier transform respectively. It can be seen from the above equation that there is a phase difference between the Fourier transforms of the two images before and after movement, and this phase difference can be obtained through the phase of the cross-power spectrum of the two images:

[0046]

[0047] Where * represents the conjugate term. Performing an inverse Fourier transform on the phase term obtained above, the peak position of the impulse function obtained corresponds to the image movement amount (Δx, Δy).

[0048] When the imaging sample is a rough object, the collected image is contaminated by strong speckle noise. At this time, the difficulty of image registration increases. The technology of in-plane displacement measurement of speckle images is used for image registration, which is called the speckle image sub-pixel registration method here. The specific operation is as follows:

[0049] The first step is to select a certain-sized image subset in the reference image and the moving image, and set its pixel size to (2M + 1), where M is the radius of the image subset, and the selection of this subset radius depends on the system parameters. The second step is to calculate the correlation function between the two regions. It is the basis for judging the similarity between the two image subsets, and a good correlation function will directly affect the calculation accuracy of the algorithm. Therefore, the selection of the correlation function is extremely important. The three commonly used cross-correlation calculation methods are the direct cross-correlation function, the normalized cross-correlation function, and the normalized cross-correlation function with zero mean. The calculation formula for the direct cross-correlation function CC is:

[0050]

[0051] Where f(x ki ,y kj ) is the reference image, g(x k ′ i ,y k ′ j ) is the moving image, the horizontal coordinate serial number ki = 1, 2, 3... 2M + 1, and the vertical coordinate serial number kj = 1, 2, 3... 2M + 1.

[0052] Normalized cross - correlation function C NCC is calculated by the following formula:

[0053]

[0054] The zero - mean normalized correlation function C ZNCC is defined as:

[0055]

[0056] where is the average gray value of the reference image; is the average gray value of the moving image.

[0057] The third step is to use the speckle fractal dimension for the next - step correlation calculation. Fractal is a measure of the similarity and scale invariance of irregular shapes, that is, it measures the irregularity and complexity of the measured area. Among the many methods for calculating the fractal dimension, the method based on the differential box - counting dimension is the simplest, easy to implement and widely used. This patent uses the box - counting dimension method to calculate the fractal dimension d, and the number of boxes N r is:

[0058]

[0059] where rl is the selected side - length value, and I' k is the size of the pixel gray - value within the rl×rl area.

[0060]

[0061] In the above formula, N(ε) represents the box - counting dimension of the area with side - length ε. When the side - length ε takes different values, the calculated fractal dimension d changes approximately linearly. Fitting it can obtain the specific value of the box - counting dimension. And if directly calculating the fractal dimension of a fixed area may have errors, so generally the calculation area is divided into several secondary sub - areas to increase the sampling quantity:

[0062]

[0063] where na represents the number of secondary sub - areas, and the secondary sub - area serial number t = 1, 2, 3... na; A and B both represent the sampling quantity of a certain secondary sub - area.

[0064] After calculating the fractal dimension of the corresponding area, relevant operations are carried out to find the displacement position.

[0065] The fourth step is to use the surface - fitting algorithm to find the sub - pixel displacement parameter. By calculating the correlation function, find the coordinates C d corresponding to the peak of the correlation coefficient:

[0066]

[0067] where d At and d Bt respectively represent the grayscales of different fractal dimensions, and are respectively the average grayscales of the above-mentioned fractal dimensions.

[0068] Using the least squares method, a binary cubic polynomial surface fitting C(x, y) is performed on the surrounding (4×4) pixel points centered on the peak coordinate of the correlation coefficient:

[0069] C(x, y) = A + By + Cx + Dxy + Ey 2 + Fx 2 + Gxy 2 + Hx 2 y + Iy 3 + Jx 3 (13)

[0070] where A, B, D, E, F, G, H, I, and J are fitting parameters, and C is the correlation coefficient corresponding to each coordinate value calculated in the previous step. Then, the position of the extreme point of the surface equation is determined. The displacement difference between the calculated extreme point and the peak point calculated by the integer pixel search is the sub-pixel displacement. Go to step 5.

[0071] Step 5: Use the Fourier ptychography reconstruction algorithm to reconstruct the cropped image to improve its resolution and obtain a restored image. To ensure the problem is solvable, the Fourier ptychography reconstruction algorithm uses the intensity constraint condition and the overlap rate constraint condition of the original image to reconstruct the object spectrum, iteratively updates the original image, obtains the synthetic spectrum, and gets the restored image.

[0072] Specifically as follows:

[0073] Step 5-1: Use an image sensor to obtain a series of low-resolution images I (n) at different positions, and this image records the light intensity information of the sample.

[0074] Taking the square root of the light intensity information can obtain the amplitude information at the corresponding scanning position

[0075] In the present invention, the low-resolution image at the rotation angle of 0° is selected for interpolation to obtain a high-resolution light intensity

[0076] image, and then combined with a high-resolution phase image with a phase of 0, an initial light field can be obtained, which is used as a high-resolution initial solution to preliminarily constrain the result B(I (0) ).

[0077] Among them, B(…) represents bilinear interpolation, and the subscript (0) represents the rotation angle. I (0) represents the low-resolution image at the position with a rotation angle of 0°.

[0078] Performing Fourier transform on it yields the corresponding spectral information in the frequency domain, that is, the initial high-resolution spectrum:

[0079]

[0080] Among them, (u0, v0) are the frequency-domain coordinates at the center of the spectral aperture corresponding to a rotation angle of 0°. u0 represents the abscissa in the frequency domain, and v0 represents the ordinate in the frequency domain; O0 is the high-resolution spectrum of the initial solution, F{…} represents performing Fourier transform on the complex amplitude, and I (0) represents the low-resolution image at the position with a rotation angle of 0°. represents the aperture function at the position with a rotation angle of 0°.

[0081] Proceed to step 5-2.

[0082] Step 5-2: In the frequency domain, select a scanning position, use the spatial coherent transfer function (CTF) of the imaging lens to constrain the initial high-resolution spectrum, intercept the spectral information within the frequency-domain sub-aperture at this position, and perform inverse Fourier transform on the above spectral information to obtain a low-resolution complex amplitude distribution in the spatial domain, that is, the target complex amplitude distribution:

[0083]

[0084]

[0085] Among them, e represents the target spectrum and the target complex amplitude to be updated; u represents the abscissa in the frequency domain, v represents the ordinate in the frequency domain, and (u, v) represents the frequency-domain coordinates. represents the target spectrum to be updated at the position with a rotation angle of n° during the j-th iteration. (u n , v n ) represents the frequency-domain coordinates of the center of the aperture corresponding to the spectrum at the position with a rotation angle of n°. represents the corresponding target complex amplitude distribution. P j (u, v) represents the aperture function at the position with a rotation angle of n° during the j-th iteration. F -1 {…} represents performing inverse Fourier transform on the spectrum.

[0086] Proceed to step 5-3.

[0087] Step 5-3: Ensure that the phase remains unchanged, substitute the amplitude information of the target at this scanning position, and update the amplitude part of the target complex amplitude. The update formula is:

[0088]

[0089] Among them, c represents the actually captured information, represents the new target complex amplitude distribution at the scanning position with a rotation angle of n° in the j-th iteration, represents the low-resolution image at the position with a rotation angle of n°, represents the actual target complex amplitude corresponding to the position with a rotation angle of n° in the j-th iteration. Go to step 5-4.

[0090] Step 5-4: Perform a Fourier transform on the updated target complex amplitude to obtain the updated target spectral information, and substitute this target spectral information into the spectral components within the previously obtained sub-aperture to complete the update of the spectrum at this scanning position. The update formula is:

[0091]

[0092]

[0093]

[0094] Among them, λ is the forgetting factor and δ is the adjustment factor. represents the new target spectral distribution at the position with an angle of n° in the j-th iteration, represents the new target complex amplitude distribution at the scanning position with an angle of n° in the j-th iteration, represents the target spectral information at the position with an angle of n° in the j-th iteration, represents the actual spectral information at the scanning position with an angle of n° in the j-th iteration.

[0095] Go to step 5-5.

[0096] Step 5-5: Re-select the scanning position and repeat steps 5-2 to 5-4 until the update of the complex amplitude information at all positions is completed, that is, the update of the spectral information within all sub-apertures in the frequency domain. At this time, the first iteration is completed. Go to step 5-6.

[0097] Step 5-6: Repeat steps 5-2 to 5-5 until the high-resolution complex amplitude of the object converges or reaches the preset number of iterations k. At this time, the spectrum is the synthetic spectrum that can achieve super-resolution imaging k represents the k-th iteration.

[0098] Perform an inverse Fourier transform on the synthetic spectrum, take the square of the modulus of the result, and obtain the high-resolution image after image reconstruction:

[0099]

[0100] Among them, I k represents the intensity of the restored image. represents the synthetic spectrum, k represents the k-th iteration, F -1 {… represents performing an inverse Fourier transform on the spectrum.

[0101] Based on the Fourier ptychography super-resolution imaging technology, in the macroscopic Fourier ptychography super-resolution imaging technology, the present invention changes the traditional matrix scanning. According to the basic principle of Fourier ptychography, it organically combines the macroscopic reflection imaging system and spectral ptychography scanning, and proposes to use a lens to perform rotational scanning on the spectral plane of the imaging system to obtain a series of conventional images under the original imaging lens aperture. Then, an iterative reconstruction algorithm is used to process the obtained images to expand the equivalent aperture of the imaging system and obtain a restored image with improved resolution, ultimately achieving super-resolution imaging of the macroscopic reflection rotational scanning imaging system and expanding the application scope of the traditional Fourier ptychography super-resolution imaging technology.

Claims

1. A space-based small satellite payload excitation imaging method based on swivel arm scanning, characterized in that Including the following steps: Step 1: Set up a macro-reflective Fourier ptychography imaging system based on rotational scanning: The Fourier ptychography imaging system based on rotational scanning includes a laser, a beam expander, a diaphragm, a convex lens, an object, an imaging lens at the reflection receiving end, a CCD detector, which are arranged coaxially in sequence, and also includes a precision electric rotary stage; wherein the imaging lens at the reflection receiving end is fixed on the CCD detector, and the imaging lens and the corresponding CCD detector are fixed on the precision electric rotary stage; the light emitted by the laser is expanded and filtered by the beam expander and the diaphragm to provide relatively uniform illumination, and then the convex lens converts the light field into parallel light to perform coherent illumination on the object, realizing far-field Fraunhofer diffraction at a finite distance; at this time, the light field distribution on the front surface of the imaging lens after the light field emitted by the object propagates over a long distance is the far-field Fraunhofer diffraction result of the object, and then go to Step 2; Step 2: Control the CCD detector mounted on the rotary stage to perform rotational scanning perpendicular to the optical path plane, and collect N low-resolution images obtained as the original image sequence; Wherein the imaging ranges of the CCD detectors overlap with each other, and the overlap rate is greater than 80%, N≥24, and then go to Step 3; Step 3: Perform distortion correction and image denoising on each image in the collected original image sequence one by one to obtain a preprocessed image sequence, and then go to Step 4; Step 4: Register the preprocessed image sequence to obtain a registered image sequence: For the specular reflection object image, use a method based on the transform domain to register the preprocessed image to obtain a registered image sequence; for the diffuse reflection object, use a registration method based on the speckle fractal dimension to obtain a registered image sequence, and then go to Step 5; Step 5: Use the Fourier ptychography reconstruction algorithm to perform reconstruction processing on the registered image sequence to obtain a restored image.

2. The method for exciting imaging of a space-based small satellite payload based on swing arm scanning according to claim 1, wherein: The object of the space-based small satellite payload excitation imaging system based on rotational arm scanning is a reflective object.

3. The method for exciting imaging of a space-based small satellite payload based on swing arm scanning according to claim 1, wherein: The light field emitted by the object enters the imaging lens after propagating over a long distance. Regarding its long-distance propagation as a single Fraunhofer far-field propagation; after intercepting its Fourier spectrum using the aperture of the imaging lens, it is then received by the CCD detector, and N low-resolution original image sequences under the imaging lens aperture are collected; the above low-resolution images record the light intensity information of the object, and taking the square root of the low-resolution images gives the amplitude information at the corresponding scanning position.

4. The space-based small satellite payload excitation imaging method based on swing arm scanning according to claim 1, characterized in that: In Step 4, use a registration method based on the transform domain and the speckle fractal dimension to register the preprocessed image sequence to obtain a registered image sequence, specifically as follows: When the FP system images a smooth object, a method in the transform domain is required to register the preprocessed image sequence between the bright-field image and the dark-field image; if the FP system images a rough object, a speckle fractal dimension registration method is required to register the preprocessed image sequence.

5. The method for exciting imaging of a space-based small satellite payload based on swing arm scanning according to claim 1, wherein: In Step 5, use the Fourier ptychography reconstruction algorithm to perform reconstruction processing on the registered image sequence to improve its resolution and obtain a restored image, specifically as follows: Step 5-1: Select one image from the registered image sequence as the reference, interpolate and magnify it to the size of the reconstructed image, and then combine it with a high-resolution image with a phase of 0 and an amplitude of 1 to obtain the initial light field. Use the initial light field as the high-resolution initial solution to perform a preliminary constraint on the reconstruction result. Perform a Fourier transform on the initial light field to obtain the corresponding spectral information in the frequency domain, that is, the initial high-resolution spectrum: Among them, (u0, v0) is the frequency-domain coordinate at the center of the spectral aperture corresponding to a rotation angle of 0°, where u0 represents the frequency-domain abscissa corresponding to 0°, and v0 represents the frequency-domain ordinate corresponding to 0°; O0 is the high-resolution spectrum of the initial solution, F{...} represents the Fourier transform of the complex amplitude, B(...) represents bilinear interpolation, and I (0) represents the low-resolution image at the position with a rotation angle of 0°, represents the aperture function at a rotation angle of 0°; Proceed to Step 5-2; Step 5-2: Select a scanning position in the frequency domain, use the coherent transfer function of the imaging lens to constrain the initial high-resolution spectrum, intercept the spectral information within the frequency-domain sub-aperture at this position, and perform an inverse Fourier transform on the above spectral information to obtain a low-resolution complex amplitude distribution in the spatial domain, that is, the target complex amplitude distribution: Among them, u represents the abscissa in the frequency domain, v represents the ordinate in the frequency domain, and (u, v) represents the frequency domain coordinates. represents the target spectrum to be updated at the position with an angle of n° in the j-th iteration. (u n , v n ) represents the frequency domain coordinates of the aperture center in the spectrum corresponding to the position with an angle of n°. represents the corresponding target complex amplitude distribution. P j (u, v) represents the aperture function at an angle of n° in the j-th iteration. F -1 {...} represents the inverse Fourier transform of the spectrum. Proceed to Step 5-3; Step 5-3: Keep the phase unchanged, substitute the amplitude information of the target at this scanning position, and update the amplitude part of the target complex amplitude. The update formula is: Among them, c represents the actually captured information. represents the new target complex amplitude distribution at the scanning position with an angle of n° in the j-th iteration. represents the low-resolution image at the position with an angle of n°. represents the actual target complex amplitude corresponding to the position with an angle of n° in the j-th iteration. Proceed to Step 5-4; Step 5-4: Perform a Fourier transform on the updated target complex amplitude to obtain the updated target spectral information. Substitute the updated target spectral information into the spectral components within the previously obtained sub-aperture to complete the update of the spectrum at this scanning position. The update formula is as follows: where λ is the forgetting factor and δ is the adjustment factor; represents the new target spectral distribution at the position with an angle of n° during the j-th iteration, represents the new target complex amplitude distribution at the scanning position with an angle of n° during the j-th iteration, represents the target spectral information at the position with an angle of n° during the j-th iteration, represents the actual spectral information at the scanning position with an angle of n° during the j-th iteration; Proceed to Step 5-5; Step 5-5: Re-select the scanning position, and repeat Steps 5-2 to 5-4 until the update of the complex amplitude information at all positions is completed, that is, the update of the spectral information within all sub-apertures in the frequency domain. At this time, the first iteration is completed, and proceed to Step 5-6; Step 5-6: Repeat Steps 5-2 to 5-5 until the high-resolution complex amplitude of the target object converges. The complex amplitude at this time is the optimal solution of the high-resolution complex amplitude of the target object, complete the reconstruction of the image, and obtain a restored image.

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