Methods and Systems for Dynamic Phase Difference Estimation in Remote Sensing Imaging

Abstract

This application relates to the field of remote sensing imaging technology, and in particular to a method and system for dynamic phase difference estimation in remote sensing imaging. The method includes: acquiring a light field image of the remote sensing target to be imaged; extracting pixels at the same angle from the light field image and fusing them to obtain a single-view image, ultimately obtaining multiple images from different viewpoints; calculating the offset matrix of the images from multiple viewpoints according to a preset strategy, and using the offset matrix to perform digital offset correction on the images from multiple viewpoints; and stitching and fusing the corrected images from multiple viewpoints to obtain the final imaging result of the target to be imaged. Therefore, the embodiments of this application can effectively remove aberrations caused by complex turbulence, significantly improving the resolution of remote sensing imaging; by fusing multi-view images through incoherent apertures, aberrations are more effectively removed, achieving large-scene, turbulence-resistant, and high-resolution imaging. It effectively solves environmental phase differences such as high-speed atmospheric turbulence in the field of remote sensing imaging.

Description

Technical Field

[0001] This application relates to the field of remote sensing imaging technology, and in particular to a method and system for dynamic phase difference estimation in remote sensing imaging. Background Technology

[0002] High-resolution optical remote sensing technology is the primary means of acquiring large-scale, high-precision, and multi-layered information about the Earth's surface, playing a crucial role in many fields such as meteorology, exploration, security, and reconnaissance. However, among related technologies, astronomical observation systems still face three main challenges:

[0003] 1. High-precision correction of optical aberrations for large-aperture lenses

[0004] High-precision remote sensing relies heavily on large-aperture optical lenses to improve light gathering and resolution. However, increasing the aperture of the imaging system makes it larger, the control system more complex, and the surface shape detection and processing accuracy difficult to guarantee, which significantly increases the optical aberration of the system and seriously reduces the actual imaging resolution and signal-to-noise ratio.

[0005] 2. Adaptive Reconstruction of Large-Scale Scenes in Complex Turbulent Environments

[0006] Complex atmospheric turbulence and thermal halo effects cause random scattering and refraction of light during propagation, resulting in environmental aberrations that rapidly reduce actual imaging resolution and signal-to-noise ratio. Adaptive optics correction systems in related technologies cannot be applied to the simultaneous correction of multiple non-uniform halo regions in a large field of view, and these systems are large, complex in structure, and extremely costly to manufacture and maintain.

[0007] 3. Incoherent optical aperture synthesis

[0008] Since the light signals of natural scenes detected by optical remote sensing are incoherent light, the traditional sensor detection process will lose the phase information of complex light fields, making it difficult to achieve multi-aperture synthesis. This leads to a trade-off between spatial resolution and angular resolution in high-dimensional light field acquisition methods, posing a severe challenge to computational reconstruction. Summary of the Invention

[0009] This application provides a method and system for dynamic phase difference estimation in remote sensing imaging, which can effectively improve resolution, solve aberrations caused by complex turbulence, and realize incoherent aperture synthesis, thereby enhancing the level of remote sensing imaging technology.

[0010] The first aspect of this application provides a method for dynamic phase difference estimation in remote sensing imaging, comprising the following steps: acquiring a light field image of the remote sensing target to be imaged; extracting pixels at the same angle from the light field image and fusing them to obtain a single-view image, ultimately obtaining multiple images from different viewpoints; calculating the offset matrix of the multiple viewpoint images according to a preset strategy, and using the offset matrix to perform digital offset correction on the multiple viewpoint images; and stitching and fusing the corrected multiple viewpoint images. This method can effectively remove atmospheric turbulence phase difference and obtain the final imaging result of the remote sensing target to be imaged.

[0011] Optionally, the step of performing digital offset correction on the images from the multiple viewpoints using the offset matrix includes: performing a two-dimensional integral on the offset matrix to obtain an aberration matrix; correcting the offset matrix according to the aberration matrix; and performing digital offset correction on the images from the multiple viewpoints using the corrected offset matrix.

[0012] Optionally, the step of stitching and fusing the corrected images from multiple perspectives to obtain the final imaging result of the remote sensing target to be imaged includes: obtaining the relative positional relationship of the corrected images from multiple perspectives; and stitching and fusing the corrected images from multiple perspectives according to the relative positional relationship to obtain the final imaging result of the remote sensing target to be imaged.

[0013] Optionally, acquiring the light field image of the remote sensing target to be imaged includes: acquiring the light field image using a light field camera; and acquiring the light field image of dense spatial sampling using a preset scanning light field imaging method.

[0014] A second aspect of this application provides a dynamic phase difference estimation system for remote sensing imaging, comprising: an acquisition module for acquiring a light field image of a remote sensing target to be imaged; a first fusion module for extracting pixels with the same angle from the light field image and fusing them to obtain a single-view image, ultimately obtaining multiple images with different viewpoints; a correction module for calculating the offset matrix of the images from multiple viewpoints according to a preset strategy, and using the offset matrix to perform digital offset correction on the images from multiple viewpoints; and a second fusion module for stitching and fusing the corrected images from multiple viewpoints to obtain the final imaging result of the remote sensing target to be imaged.

[0015] Optionally, the correction module is configured to: perform a two-dimensional integral on the offset matrix to obtain an aberration matrix; correct the offset matrix according to the aberration matrix; and use the corrected offset matrix to perform digital offset correction on the images of the multiple viewpoints.

[0016] Optionally, the first fusion module is used to: acquire the relative positional relationship of the corrected images from multiple perspectives; and stitch and fuse the corrected images from multiple perspectives according to the relative positional relationship to obtain the final imaging result of the remote sensing target to be imaged.

[0017] Optionally, the acquisition module is used to: acquire light field images using a light field camera; and acquire light field images of dense space sampling using a preset scanning light field imaging method.

[0018] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the dynamic phase difference estimation method for remote sensing imaging as described in the above embodiments.

[0019] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the method for dynamic phase difference estimation of remote sensing imaging as described in the above embodiments.

[0020] Therefore, this application has at least the following beneficial effects:

[0021] It can effectively remove aberrations caused by complex turbulence, significantly improving the resolution of remote sensing imaging; by fusing multi-view images through incoherent apertures, it can more effectively remove aberrations, achieving large-scene, turbulence-resistant, and high-resolution imaging. It effectively solves environmental aberrations such as high-speed atmospheric turbulence in the field of remote sensing imaging.

[0022] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. Attached Figure Description

[0023] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0024] Figure 1 This is a flowchart of a method for dynamic phase difference estimation in remote sensing imaging provided according to an embodiment of this application;

[0025] Figure 2 This is a block diagram of a dynamic phase difference estimation system for remote sensing imaging provided according to an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0027] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0028] High-resolution optical remote sensing technology is the primary means of acquiring large-scale, high-precision, and multi-layered spatial information about the Earth's surface, playing a crucial role in many fields such as meteorology, exploration, security, and reconnaissance. The development focus of high-resolution optical remote sensing lies in improving high-resolution wide-swath imaging and agile imaging capabilities.

[0029] To address the three problems mentioned in the background art regarding high-precision correction of optical aberrations in large-aperture lenses, adaptive reconstruction of large-scale scenes under complex turbulent environments, and incoherent aperture synthesis in remote sensing observation systems, this application provides a method and system for dynamic phase difference estimation in remote sensing imaging. The following description will refer to the accompanying drawings.

[0030] like Figure 1 As shown, the method for dynamic phase difference estimation in remote sensing imaging includes the following steps:

[0031] In step S101, the light field image of the remote sensing target to be imaged is acquired.

[0032] It is understood that the embodiments of this application can acquire light field images using a conventional light field camera; and acquire dense spatially sampled light field images using a preset scanning light field imaging method. The preset scanning light field imaging method can be selected according to actual needs and is not specifically limited thereto.

[0033] In step S102, pixels with the same angle are extracted from the light field image and fused to obtain a single-view image, and finally multiple images with different viewpoints are obtained.

[0034] It is understood that the embodiments of this application can divide the acquired light field image into images from multiple perspectives.

[0035] In step S103, the offset matrix of images from multiple viewpoints is calculated according to a preset strategy, and the offset matrix is ​​used to perform digital offset correction on the images from multiple viewpoints.

[0036] The preset strategy may include correlation method or optical flow method, which can be set according to actual needs. For example, in the embodiments of this application, the offset matrix can be obtained by offset estimation of multiple views through correlation method or optical flow method.

[0037] It is understood that the embodiments of this application can effectively remove atmospheric turbulence phase difference through offset matrix correction.

[0038] In this embodiment of the application, digital offset correction is performed on images from multiple viewpoints using an offset matrix, including: performing two-dimensional integration on the offset matrix to obtain an aberration matrix; correcting the offset matrix based on the aberration matrix; and performing digital offset correction on images from multiple viewpoints using the corrected offset matrix.

[0039] It is understood that, in the embodiments of this application, the aberration matrix can be obtained by two-dimensional integration of the offset matrix, and the Zernike defocus term can be removed from the phase difference matrix and then two-dimensional differenced to obtain the accurate offset matrix, thereby realizing digital offset correction of multi-view images.

[0040] Specifically, embodiments of this application can use digital adaptive optics to estimate aberrations in a multi-view imaging queue and perform aberration correction. The specific steps are as follows: Embodiments of this application can use meta-imaging technology to adaptively extract globally spatially non-uniform wavefront phase information from high-dimensional optical signals, further realizing dynamic aberration correction in a large range and multiple regions, effectively improving the optical remote sensing imaging capability under complex turbulent environments; at the same time, embodiments of this application can apply computational imaging principles to achieve accurate measurement and correction of optical aberrations in large-aperture systems at low cost and in a miniaturized manner based on existing lens manufacturing levels and existing systems, effectively improving the level of remote sensing imaging technology.

[0041] In step S104, the corrected images from multiple perspectives are stitched and fused to obtain the final imaging result of the remote sensing target to be imaged.

[0042] It is understood that after offset matrix correction, the embodiments of this application further obtain the final imaging result through image fusion in order to further remove atmospheric turbulence phase difference.

[0043] In this embodiment of the application, the images of the corrected multiple perspectives are stitched and fused to obtain the final imaging result of the remote sensing target to be imaged, including: obtaining the relative positional relationship of the images of the corrected multiple perspectives; and stitching and fusing the images of the corrected multiple perspectives according to the relative positional relationship to obtain the final imaging result of the remote sensing target to be imaged.

[0044] It is understood that the embodiments of this application can directly stitch and fuse the offset images according to the relative positional relationship of the offset to generate the final imaging result of the remote sensing target to be imaged.

[0045] Specifically, the embodiments of this application can employ meta-imaging technology, based on micro-nano optical devices, to break through the traditional optoelectronic sensor architecture and propose a novel high-efficiency coupling acquisition mechanism for high-resolution light field imaging. This mechanism synthesizes the aperture of incoherent beams, enabling ultra-long-distance high-resolution sensing in complex turbulent environments. Thus, the corrected images from multiple perspectives can be stitched and fused together through the incoherent aperture to achieve high-resolution imaging.

[0046] The dynamic phase difference estimation method for remote sensing imaging proposed in the embodiments of this application can effectively remove aberrations caused by complex turbulence, thereby significantly improving the resolution of remote sensing imaging. By fusing multi-view images through incoherent aperture, aberrations can be removed more effectively, enabling large-scene, turbulence-resistant, and high-resolution imaging, and effectively solving environmental phase differences such as high-speed atmospheric turbulence in the field of remote sensing imaging.

[0047] Next, referring to the accompanying drawings, a dynamic phase difference estimation system for remote sensing imaging based on embodiments of this application is described.

[0048] Figure 2 This is a block diagram of a dynamic phase difference estimation system for remote sensing imaging according to an embodiment of this application.

[0049] like Figure 2 As shown, the system 100 for dynamic phase difference estimation in remote sensing imaging includes: an acquisition module 110, a first fusion module 120, a correction module 130, and a second fusion module 140.

[0050] The acquisition module 110 is used to acquire the light field image of the remote sensing target to be imaged; the first fusion module 120 is used to extract pixels with the same angle from the light field image and fuse them to obtain a single-view image, and finally obtain multiple images with different viewpoints; the correction module 130 is used to calculate the offset matrix of the images of multiple viewpoints according to a preset strategy, and use the offset matrix to perform digital offset correction on the images of multiple viewpoints; the second fusion module 140 is used to stitch and fuse the corrected images of multiple viewpoints to obtain the final imaging result of the remote sensing target to be imaged.

[0051] In this embodiment of the application, the correction module 130 is used to: perform two-dimensional integration on the offset matrix to obtain an aberration matrix; correct the offset matrix according to the aberration matrix; and use the corrected offset matrix to perform digital offset correction on images from multiple viewpoints.

[0052] In this embodiment of the application, the first fusion module 120 is used to: acquire the relative positional relationship of the corrected images from multiple perspectives; and stitch and fuse the corrected images from multiple perspectives according to the relative positional relationship to obtain the final imaging result of the remote sensing target to be imaged.

[0053] In this embodiment of the application, the acquisition module 110 is used to: acquire light field images using a light field camera; and acquire light field images of dense space sampling using a preset scanning light field imaging method.

[0054] It should be noted that the foregoing explanation of the embodiment of the dynamic phase difference estimation method for remote sensing imaging also applies to the dynamic phase difference estimation system for remote sensing imaging in this embodiment, and will not be repeated here.

[0055] The dynamic phase difference estimation system for remote sensing imaging proposed in the embodiments of this application can effectively remove aberrations caused by complex turbulence, thereby significantly improving the resolution of remote sensing imaging. By fusing multi-view images through incoherent aperture, aberrations can be removed more effectively, enabling large-scene, turbulence-resistant, and high-resolution imaging, and effectively solving environmental phase differences such as high-speed atmospheric turbulence in the field of remote sensing imaging.

[0056] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0057] The memory 301, the processor 302, and the computer program stored on the memory 301 and capable of running on the processor 302.

[0058] When the processor 302 executes the program, it implements the dynamic phase difference estimation method for remote sensing imaging provided in the above embodiments.

[0059] Furthermore, electronic devices also include:

[0060] Communication interface 303 is used for communication between memory 301 and processor 302.

[0061] The memory 301 is used to store computer programs that can run on the processor 302.

[0062] The memory 301 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0063] If the memory 301, processor 302, and communication interface 303 are implemented independently, then the communication interface 303, memory 301, and processor 302 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0064] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0065] Processor 302 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of this application.

[0066] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for dynamic phase difference estimation in remote sensing imaging.

[0067] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0068] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0069] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0070] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0071] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0072] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for dynamic phase difference estimation in remote sensing imaging, characterized in that, Includes the following steps: Acquire light field images of the remote sensing target to be imaged; Pixels at the same angle are extracted from the light field image and fused to obtain a single-view image, and finally multiple images with different viewpoints are obtained. The offset matrix of images from multiple viewpoints is calculated according to a preset strategy, and digital offset correction is performed on the images from multiple viewpoints using the offset matrix. The digital offset correction of the images from multiple viewpoints using the offset matrix includes: performing two-dimensional integration on the offset matrix to obtain an aberration matrix; correcting the offset matrix according to the aberration matrix; and performing digital offset correction on the images from multiple viewpoints using the corrected offset matrix. Meta-imaging technology is used to adaptively extract globally spatially non-uniform wavefront phase information from high-dimensional optical signals, further realizing dynamic aberration correction in a large range and multiple regions. The corrected images from multiple perspectives are stitched and fused to obtain the final imaging result of the remote sensing target to be imaged.

2. The method according to claim 1, characterized in that, The process of stitching and fusing the corrected images from multiple perspectives to obtain the final imaging result of the remote sensing target to be imaged includes: Obtain the relative positional relationships of images from multiple corrected viewpoints; Based on the relative positional relationship, the images from multiple corrected perspectives are stitched and fused to obtain the final imaging result of the remote sensing target to be imaged. In this process, meta-imaging technology is used, and based on micro-nano optical devices, an efficient coupling acquisition mechanism for high-resolution light field imaging is proposed to synthesize the aperture of incoherent beams.

3. The method according to any one of claims 1-2, characterized in that, The acquisition of the light field image of the remote sensing target to be imaged includes: Acquire light field images using a light field camera; A light field image of dense spatial sampling is obtained using a preset scanning light field imaging method.

4. A dynamic phase difference estimation system for remote sensing imaging, characterized in that, include: The acquisition module is used to acquire light field images of the remote sensing target to be imaged; The first fusion module is used to extract pixels with the same angle from the light field image and fuse them to obtain a single-view image, and finally obtain multiple images with different viewpoints. The correction module is used to calculate the offset matrix of images from multiple viewpoints according to a preset strategy, and to perform digital offset correction on the images from multiple viewpoints using the offset matrix. The step of performing digital offset correction on the images from multiple viewpoints using the offset matrix includes: performing two-dimensional integration on the offset matrix to obtain an aberration matrix; correcting the offset matrix according to the aberration matrix; and performing digital offset correction on the images from multiple viewpoints using the corrected offset matrix. Meta-imaging technology is used to adaptively extract globally spatially inconsistent wavefront phase information from high-dimensional optical signals, further realizing dynamic aberration correction over a large area and multiple regions. The second fusion module is used to stitch and fuse the corrected images from multiple perspectives to obtain the final imaging result of the remote sensing target to be imaged.

5. The system according to claim 4, characterized in that, The first fusion module is used for: Obtain the relative positional relationships of images from multiple corrected viewpoints; Based on the relative positional relationship, the images from multiple corrected perspectives are stitched and fused to obtain the final imaging result of the remote sensing target to be imaged. In this process, meta-imaging technology is used, and based on micro-nano optical devices, an efficient coupling acquisition mechanism for high-resolution light field imaging is proposed to synthesize the aperture of incoherent beams.

6. The system according to any one of claims 4-5, characterized in that, The acquisition module is used for: Acquire light field images using a light field camera; A light field image of dense spatial sampling is obtained using a preset scanning light field imaging method.

7. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for dynamic phase difference estimation for remote sensing imaging as described in any one of claims 1-3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for dynamic phase difference estimation for remote sensing imaging as described in any one of claims 1-3.

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