Critical diffraction limited super large width high resolution optical remote sensing imaging method

Abstract

The application discloses a critical diffraction limit super-large-width high-resolution optical remote sensing imaging method, which comprises the following steps: S1, setting a sub-pixel dynamic coding board on the surface of a TDI CMOS device; S2, sampling a low-resolution image by the TDI CMOS device to obtain a coded image; S3, replacing the sub-pixel dynamic coding board, repeating the steps S1 to S2 to obtain a plurality of coded images; and S4, reconstructing the plurality of coded images to obtain a high-resolution image. The application breaks through the bottleneck of resolution improvement technology of a super-large-width optical remote sensing camera.

Description

Technical Field

[0001] This invention belongs to the field of aerospace optical remote sensing technology, and particularly relates to a critical diffraction limit ultra-wide swath high-resolution optical remote sensing imaging method. Background Technology

[0002] The spatial bandwidth product describes the information transmission capability of an imaging system. The spatial bandwidth product of an optical imaging system equals the product of the effective field of view and the passband area determined by the system's cutoff frequency. The spatial bandwidth product is invariant; the larger the product, the more difficult the design and manufacturing become. According to the spatial bandwidth product theory, there is an upper limit to the information acquisition capability of an optical remote sensing camera system. The broadening of the signal acquired by the optical camera system in the spatial and frequency domains is mutually restrictive; swath width and resolution cannot be simultaneously achieved. Furthermore, traditional optical systems are limited by the long focal length requirements (high spatial resolution) of remote sensing cameras, thus limiting the achievable field of view.

[0003] Reducing the pixel size of detectors increases the pixel scale of individual devices and also reduces the overall size, weight, and power consumption of the optical imaging system, thus significantly saving costs. However, small-pixel detector devices require high-quality, high-Nyquist-frequency optical lenses, posing new challenges to traditional optical designs. Traditional optical designs improve image quality at high Nyquist frequencies by modifying aspherical mirrors to higher-order freeform surfaces or adding correction mirrors, but this significantly increases system complexity, fabrication difficulty, and assembly complexity, requiring substantial investment.

[0004] Due to the high difficulty in engineering large optical components, the diffraction-limited cutoff frequency of optical systems, and the boundary limitations of traditional system design methods, the cost of developing higher resolution and wider swath optical remote sensing cameras for Earth observation is becoming increasingly high.

[0005] In summary, there are serious technical bottlenecks in improving the resolution capability of ultra-wide-swath optical remote sensing cameras. Traditional optical designs struggle to achieve the specifications of a 0.25m spatial resolution and a 150km wide-swath optical imaging system. Summary of the Invention

[0006] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide a critical diffraction limit ultra-wide resolution optical remote sensing imaging method, which breaks through the technical bottleneck of resolution improvement of ultra-wide resolution optical remote sensing cameras.

[0007] The objective of this invention is achieved through the following technical solution: a critical diffraction limit ultra-wide-swath high-resolution optical remote sensing imaging method, comprising: step S1: setting a sub-pixel dynamic encoding plate on the surface of a TDI CMOS device; step S2: sampling a low-resolution image through the TDI CMOS device to obtain a frame of encoded image; step S3: replacing different sub-pixel dynamic encoding plates and repeating steps S1 to S2 to obtain multiple frames of encoded images; step S4: reconstructing the multiple frames of encoded images to obtain a high-resolution image.

[0008] In the above-mentioned critical diffraction limit ultra-wide-angle high-resolution optical remote sensing imaging method, the pixel size of the TDI CMOS device is 7µm, and the pixel size of the sub-pixel dynamic coding plate is 3.5µm.

[0009] In the above-mentioned critical diffraction limit ultra-wide resolution optical remote sensing imaging method, each pixel of the TDI CMOS device corresponds to a pixel of a 2×2 sub-pixel dynamic coding plate.

[0010] In the aforementioned critical diffraction limit ultra-wide resolution optical remote sensing imaging method, in the sub-pixel dynamic coding plate, the sub-pixel codes of the first two columns are the same as the sub-pixel codes of the adjacent last two columns.

[0011] In the above-mentioned critical diffraction limit ultra-wide high-resolution optical remote sensing imaging method, the encoding method for each sub-pixel dynamic encoding board includes: randomly generating W sets of encoding matrices, where W is a positive integer; solving the condition number of the encoding matrix; sorting the condition numbers of the encoding matrix from smallest to largest to obtain a sequence number; performing cross-correlation calculation on the encoding matrix corresponding to each sequence number to obtain the encoding matrix with the smallest cross-correlation that is equal to the number of sub-pixel dynamic encoding boards; and assigning each encoding matrix with the smallest cross-correlation to the corresponding sub-pixel dynamic encoding board.

[0012] In the above-mentioned critical diffraction limit ultra-wide resolution optical remote sensing imaging method, W≥1000.

[0013] In the above-mentioned critical diffraction limit ultra-wide swath high-resolution optical remote sensing imaging method, the value of the encoding matrix is ​​0,1.

[0014] In the above-mentioned critical diffraction limit ultra-wide swath high-resolution optical remote sensing imaging method, the condition number of the encoding matrix is ​​obtained by the following formula:

[0015]

[0016]

[0017] ∑1=diag(σ1,σ2,…,σ r );

[0018] Among them, C iLet A be the condition number of the i-th encoding matrix. i Let be the encoding matrix of the i-th group, min(·) represents the minimum value operator, and max(·) represents the maximum value operator. For encoding matrix A i The diagonal matrix after singular value decomposition, where ∑1 represents the non-negative real numbers on the diagonal, and σ1 represents the encoding matrix A. i The first singular value, σ2, is the encoding matrix A. i The second singular value, σ r For encoding matrix A i The r-th singular value, where r is the encoding matrix A. i The number of non-zero singular values.

[0019] A critical diffraction limit ultra-wide-swath high-resolution optical remote sensing imaging device includes: a first module for setting a sub-pixel dynamic coding plate on the surface of a TDI CMOS device; a second module for sampling a low-resolution image through the TDI CMOS device to obtain a frame of coded image; a third module for replacing different sub-pixel dynamic coding plates to obtain multiple frames of coded images; and a fourth module for reconstructing the multiple frames of coded images to obtain a high-resolution image.

[0020] An electronic device includes: a memory for storing computer-readable instructions; and a processor for executing the computer-readable instructions to perform a critical diffraction limit ultra-wide-angle high-resolution optical remote sensing imaging method.

[0021] Compared with the prior art, the present invention has the following advantages:

[0022] This invention only requires cutting into (or out of) the sub-pixel dynamic encoding plate in front of the focal plane to achieve resolution improvement, breaking through the technical bottleneck of resolution improvement for ultra-wide-angle optical remote sensing cameras. It has low engineering implementation difficulty and strong scalability. Attached Figure Description

[0023] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0024] Figure 1 This is a schematic diagram of the optical dynamic coding super-resolution imaging link provided in an embodiment of the present invention;

[0025] Figure 2 This is a schematic diagram of the layout of the encoder plate in the imaging system according to an embodiment of the present invention;

[0026] Figure 3(a) is a schematic diagram of the optical dynamic encoding method of the linear TDI CMOS detector after adding an optical encoding board according to an embodiment of the present invention;

[0027] Figure 3(b) is a schematic diagram of the optical dynamic encoding method of the linear TDI CMOS detector before adding an optical encoding board according to an embodiment of the present invention;

[0028] Figure 4(a) is a local image block correspondence diagram provided in an embodiment of the present invention;

[0029] Figure 4(b) is a schematic diagram of the encoding matrix construction provided in an embodiment of the present invention;

[0030] Figure 5 This is a flowchart of the optical dynamic coding imaging simulation and reconstruction process of the linear array detector provided in the embodiments of the present invention. Detailed Implementation

[0031] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0032] Critical diffraction-limited ultra-wide-swath high-resolution optical remote sensing imaging system, such as Figure 1 As shown, an optical dynamic coding board is inserted in front of the focal plane to output N remote sensing images with different codes at a resolution of 0.5m. After being transmitted to the ground, super-resolution reconstruction is performed using the N optically coded images and the calibration data of the 0.25m optical coding board, followed by image quality enhancement processing, and finally the final image product is obtained.

[0033] This embodiment provides a critical diffraction-limited ultra-wide-swath high-resolution optical remote sensing imaging method, which includes:

[0034] Step S1: Place the sub-pixel dynamic encoding board on the surface of the TDI CMOS device;

[0035] Step S2: Sample the low-resolution image using a TDI CMOS device to obtain a frame of coded image;

[0036] Step S3: Replace with different sub-pixel dynamic coding boards and repeat steps S1 to S2 to obtain multi-frame coded images;

[0037] Step S4: Reconstruct the multi-frame coded images to obtain a high-resolution image.

[0038] The TDI CMOS device has a pixel size of 7µm, and the sub-pixel dynamic encoding board has a pixel size of 3.5µm. Each pixel of the TDI CMOS device corresponds to 2×2 pixels of the sub-pixel dynamic encoding board.

[0039] In the sub-pixel dynamic encoding panel, the sub-pixel codes of the first two columns are the same as the sub-pixel codes of the two adjacent columns.

[0040] The encoding method for each sub-pixel dynamic coding board includes: randomly generating W sets of coding matrices, where W is a positive integer; solving for the condition number of the coding matrix; sorting the condition numbers of the coding matrices from smallest to largest to obtain a sequence number; performing cross-correlation calculation on the coding matrix corresponding to each sequence number to obtain the coding matrix with the smallest cross-correlation that is equal to the number of sub-pixel dynamic coding boards; and assigning each coding matrix with the smallest cross-correlation to the corresponding sub-pixel dynamic coding board.

[0041] W≥1000. The values ​​of the encoding matrix are [0,1].

[0042] The condition number of the encoding matrix is ​​obtained by the following formula:

[0043]

[0044] ∑1=diag(σ1,σ2,…,σ r );

[0045] Among them, C i Let A be the condition number of the i-th encoding matrix. i Let be the encoding matrix of the i-th group, min(·) represents the minimum value operator, and max(·) represents the maximum value operator. For encoding matrix A i The diagonal matrix after singular value decomposition, i.e. ∑1 represents the non-negative real numbers on the diagonal, and σ1 represents the encoding matrix A. i The first singular value, σ2, is the encoding matrix A. i The second singular value, σ r For encoding matrix A i The r-th singular value, where r is the encoding matrix A. i The number of non-zero singular values.

[0046] In this embodiment, the sub-pixel dynamic encoding board is integrated close to the surface of the TDI CMOS device. A 7µm single pixel corresponds to a pixel in the 2×23.5µm sub-pixel optical encoding board. The TDI CMOS device performs 32 levels of identical encoding on the same ground object and accumulates the signals to output the first encoded linear array image. The next 32 levels of TDI CMOS area use different encoding methods to acquire linear array images of the same ground object, and so on, acquiring 8 linear array images with different encodings (i.e., multi-frame encoded images).

[0047] In the sub-pixel dynamic coding board, in the first 32-level coding, the first two columns of sub-pixel codes are the same as the last two columns, and the same code can be applied to the same ground feature. After integration and accumulation, the images corresponding to the two columns of sub-pixel codes in the first 32-level coding are sensed by one detector. The first two columns of sub-pixel codes in the first 32-level coding are completely different from the first two columns of sub-pixel codes in the (N-1)th and Nth 32-level coding, thus there are N different codes. By pushing and scanning, sub-pixel coding sampling is performed on the same column of ground features to achieve optical dynamic coding.

[0048] The optimal coding method is a random, incoherent, locally optimal coding optimization method. Specifically:

[0049] Step 1: Randomly generate W groups (W≥1000) of encoding matrices A with values ​​in the range [0,1]. i (i = 1, 2, ..., W), the encoding matrix has a size of N × B pixels;

[0050] Step 2: Solve for each encoding matrix A i Condition number C of (i=1,2,…,W) i (i = 1, 2, ..., W):

[0051]

[0052] Among them, A i Let be the i-th linear array random encoding matrix, min(·) denotes the minimum value operator, and max(·) denotes the maximum value operator. U and V are the singular values ​​σ i The left and right singular vectors, ∑1=diag(σ1,σ2,…,σ r );

[0053] Step 3: For C i Sort the i (i = 1, 2, ..., W) in ascending order and store them in sequence number K. i In (i = 1, 2, ..., W);

[0054] Step 4: Assume j is in [K1, K int[W·P]Let P = 10% and int[·] represent the integer operator. Then, calculate N (N < int[W·P]) minimum cross-correlation coding matrices according to the following formula:

[0055]

[0056] Where E[] represents the average value calculation. This is a product operation for variable j from 1 to N, where j is the variable, K1 is the first value of array K, and K... int[W·P] Let int[W·P] be the value of array K.

[0057] This embodiment uses an optimized optical dynamic coding matrix group to perform pixel subdivision to achieve sub-pixel sampling, and achieves super-resolution imaging of the TDI CMOS device imaging system through partitioned sub-pixel coding technology; it uses local compressed sensing technology to reconstruct low-resolution images, and obtains high-resolution images through multiple coding sampling reconstructions; it uses a cooperative energy compensation algorithm for post-processing to remove the linear inconsistency effect that is prone to occur in the optical coding imaging of linear array detectors.

[0058] Optical dynamic coding is used for pixel subdivision to achieve sub-pixel sampling. The layout of the coding plate in the imaging system is as follows: Figure 2 As shown, the sub-pixel dynamic encoding board is integrated close to the surface of the TDI CMOS device. A 7µm single pixel corresponds to a 2×23.5µm sub-pixel optical encoding board. The same ground object is encoded in 32 levels, and the signal is accumulated to output the first encoded linear array image. Then, the next 32 levels of TDI CMOS area are used to acquire linear array images of the same ground object using different encoding methods. This process is repeated to acquire 8 linear array images with different encodings. The 8 low-resolution encoded images are sparsely reconstructed to obtain a high-resolution image. It is expected that the image resolution will be improved to twice that of the original.

[0059] The optical dynamic coding method of the linear TDI CMOS detector is shown in Figures 3(a) and 3(b). In the first 32-level coding, the first two columns of sub-pixel coding are the same as the last two columns of coding, and the same coding can be performed on the same ground feature. After integration and accumulation, the image corresponding to the two columns of sub-pixel coding in the first 32-level coding is sensed by one column of detectors. The first two columns of sub-pixel coding in the first 32-level coding are completely different from the first two columns of sub-pixel coding in the (N-1)th and Nth 32-level coding, so there are N different codings. By pushing and scanning, sub-pixel coding sampling is performed on the same column of ground features to realize optical dynamic coding.

[0060] The local compressed sensing coding method is as follows: A high-resolution coded mask image of N×B pixels is divided into image blocks of size B×B. Let x... j (j=1,2,…,n,n=(N / B 2() is the column vector form of the j-th block. Let one detector pixel correspond to a high-resolution scene image within an R×R region, and the size of the measured image block be... Compressed sampling of a block can then be expressed as:

[0061] y j =Φ j x j (1)

[0062] Among them, y j For the corresponding x j The column vector of the measured image patch, Φ j For K 2 ×B 2 The size of the encoding matrix is ​​related to the actual encoding mask. As shown in Figure 4(a), an 8×8 pixel encoding mask corresponds to a 4×4 pixel detector array, where each detector pixel measures the light field intensity encoded by a 2×2 pixel subarray. After focal plane optical encoding, the detector does not obtain a high-resolution image of the observed scene, but rather a resolution-compressed sample, which needs to be reconstructed using a sparse optimization algorithm. The resolution of the image is not determined by the detector, but by the pixel size of the encoding mask. Since the pixel size of the encoding mask is smaller than that of the detector pixel, the resolution of the imaging system can be significantly improved. Figure 4(b) shows the Φ encoding using the high-resolution encoding mask corresponding to Figure 4(a). j During the construction process, empty positions in the matrix are filled with zeros.

[0063] By changing different coding masks, L measurements were performed, resulting in L pairs of coding matrices and measurement matrices for the same scene. Assume the high-resolution scene image X is of size N×B, and the measurement image Y acquired by the detector is... The size and local compressed sensing reconstruction process are as follows:

[0064] Step 1: Combine the high-resolution scene image X and the coded mask image. The image is divided into B×B blocks, and the measurement image is divided into K×K blocks. Following the method described above, a compressed sampling model for the i-th measurement can be constructed.

[0065]

[0066] in, Let represent the column vector, encoding matrix, and high-resolution scene image block column vector of the i-th and j-th measurement image block, respectively.

[0067] Step 2: Place L items Extend and splice along the column vector direction to construct an L-time measurement column vector. Similarly, construct L-fold scene images. and encoding matrix Obtain the L-time measurement compressed sampling model

[0068]

[0069] Step 3: Divide remote sensing images of different types, such as forests and cities, into B×B image blocks, and construct a complete dictionary using the K-SVD method to obtain the remote sensing dictionary set ψ;

[0070] Step 4: Using the remote sensing dictionary set ψ, solve equation (3) using the StOMP algorithm, and then solve for the estimated values ​​of all high-resolution scene image patches to obtain the initial values ​​of the high-resolution scene images.

[0071] Energy compensation is performed on high-resolution scene images using the following method:

[0072] Assuming that each pixel in a high-resolution scene image patch has the same compensated gray value, the high-resolution scene image X and the coded mask image are... The image is divided into R×R size blocks, each corresponding to a detector pixel value. The energy compensation value for the m-th image block is then...

[0073]

[0074] Where av(·) is the averaging operator, P m (·) is the operator for retrieving the m-th R×R size image block, D(e m This represents constructing an image block of size R×R, where each pixel in the image block has a value of e. m , This represents the m-th R×R image block of the encoded mask image during the i-th measurement. Let represent the measurement value of the m-th detector in the i-th measurement. Solving by setting the first-order partial derivative to zero, we obtain...

[0075]

[0076] Energy compensation is performed for each high-resolution scene image patch using the following formula.

[0077]

[0078] Among them, X L ′ represents the final reconstructed high-resolution scene image.

[0079] By fully utilizing the prior knowledge of the continuity of the reconstructed image, the linear inconsistency effect that is prone to occur in the optical coding imaging of linear array detectors is eliminated.

[0080] Linear array detector optical dynamic coding imaging simulation, such as Figure 5As shown, the high-resolution linear array original image and the high-resolution linear array optical coding image are first multiplied to obtain the high-resolution linear array coded imaging image. Then, a 2×2 downsampling operation is performed to obtain the low-resolution coded linear array image. A one-dimensional vibration model is then set up, and the low-resolution coded images of the multi-linear array considering the vibration effect are stitched together according to the model. Super-resolution reconstruction is performed with reference to the high-resolution coded image. Finally, energy compensation is performed to reduce the linear inconsistency effect.

[0081] The initial simulation extracts the actual optical coding board calibration data as the optical coding image for simulation. Since the optical coding and detector are not strictly aligned, there are gray coding areas, as well as geometric distortion and blurring. The simulation has integrated multiple actual error sources, and the simulation results are closer to the actual imaging situation.

[0082] Simulation analysis using target images collected in the laboratory revealed that only two black ring targets were visible in the low-resolution images, while six black ring targets could be distinguished in the high-resolution images. This distinction was achieved starting with the reconstructed image obtained using four optically encoded samplings, and the image quality improved with increasing sampling frequency.

[0083] This embodiment also provides a critical diffraction limit ultra-wide-swath high-resolution optical remote sensing imaging device, which includes: a first module for setting a sub-pixel dynamic coding plate on the surface of a TDI CMOS device; a second module for sampling a low-resolution image through the TDI CMOS device to obtain a frame of coded image; a third module for replacing different sub-pixel dynamic coding plates to obtain multiple frames of coded images; and a fourth module for reconstructing multiple frames of coded images to obtain a high-resolution image.

[0084] An electronic device includes: a memory for storing computer-readable instructions; and a processor for executing the computer-readable instructions to perform a critical diffraction limit ultra-wide-angle high-resolution optical remote sensing imaging method.

[0085] This embodiment only requires cutting into (or out of) the sub-pixel dynamic encoding plate in front of the focal plane to achieve resolution improvement, breaking through the technical bottleneck of resolution improvement for ultra-wide-angle optical remote sensing cameras. It has low engineering implementation difficulty and strong scalability.

[0086] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

Claims

1. A critical diffraction-limited, ultra-wide-swath, high-resolution optical remote sensing imaging method, characterized in that... include: Step S1: Place the sub-pixel dynamic encoding board on the surface of the TDI CMOS device; Step S2: Sample the low-resolution image using a TDI CMOS device to obtain a frame of coded image; Step S3: Replace with different sub-pixel dynamic coding boards and repeat steps S1 to S2 to obtain multi-frame coded images; Step S4: Reconstruct the multi-frame coded images to obtain a high-resolution image; In the sub-pixel dynamic coding panel, the sub-pixel codes of the first two columns are the same as the sub-pixel codes of the two adjacent columns that follow. The encoding methods for each sub-pixel dynamic coding panel include: Randomly generate W sets of encoding matrices, where W is a positive integer; Find the condition number of the encoding matrix; Sort the condition numbers of the encoding matrix in ascending order to obtain the sequence numbers; Cross-correlation calculation is performed on the coding matrix corresponding to each sequence number to obtain the coding matrix with the minimum cross-correlation that is equal to the number of sub-pixel dynamic coding plates. Each coding matrix with the minimum cross-correlation is assigned to the corresponding sub-pixel dynamic coding plate.

2. The critical diffraction limit ultra-wide swath high-resolution optical remote sensing imaging method according to claim 1, characterized in that: The TDI CMOS device has a pixel size of 7µm, and the sub-pixel dynamic encoding board has a pixel size of 3.5µm.

3. The critical diffraction limit ultra-wide swath high-resolution optical remote sensing imaging method according to claim 2, characterized in that: Each pixel of a TDI CMOS device corresponds to a pixel of a 2×2 sub-pixel dynamic encoding board.

4. The critical diffraction limit ultra-wide swath high-resolution optical remote sensing imaging method according to claim 1, characterized in that: W≥1000。 5. The critical diffraction limit ultra-wide swath high-resolution optical remote sensing imaging method according to claim 1, characterized in that: The numerical value of the encoding matrix is .

6. The critical diffraction limit ultra-wide swath high-resolution optical remote sensing imaging method according to claim 1, characterized in that: The condition number of the encoding matrix is ​​obtained by the following formula: ； ； ； in, For the first The condition number of the group coding matrix. For the first Group coding matrix, Characterizes the minimum value operator. Characterizes the maximum value operator. Encoding matrix The diagonal matrix after singular value decomposition, The non-negative real numbers on the diagonal. Encoding matrix The first singular value, Encoding matrix The second singular value, Encoding matrix The r-th singular value, Encoding matrix The number of non-zero singular values.

7. A critical diffraction-limited ultra-wide-swath high-resolution optical remote sensing imaging device, characterized in that... include: The first module is used to place the sub-pixel dynamic encoding board on the surface of the TDI CMOS device; The second module is used to sample a low-resolution image using a TDI CMOS device to obtain a frame of coded image; The third module is used to replace different sub-pixel dynamic coding boards to obtain multi-frame encoded images; The fourth module is used to reconstruct high-resolution images from multiple coded frames; In the sub-pixel dynamic coding panel, the sub-pixel codes of the first two columns are the same as the sub-pixel codes of the two adjacent columns that follow. The encoding methods for each sub-pixel dynamic coding panel include: Randomly generate W sets of encoding matrices, where W is a positive integer; Find the condition number of the encoding matrix; Sort the condition numbers of the encoding matrix in ascending order to obtain the sequence numbers; Cross-correlation calculation is performed on the coding matrix corresponding to each sequence number to obtain the coding matrix with the minimum cross-correlation that is equal to the number of sub-pixel dynamic coding plates. Each coding matrix with the minimum cross-correlation is assigned to the corresponding sub-pixel dynamic coding plate.

8. An electronic device, characterized in that, include: Memory: Used to store computer-readable instructions; as well as Processor: for executing the computer-readable instructions to perform the method as described in any one of claims 1 to 6.

Images