Multi-mode resonance photon calculation spectral imaging method and architecture

Abstract

The invention provides a multi-mode resonance photon calculation spectral imaging method and architecture, and the method comprises the steps: carrying out the wavelength selection and intensity modulation of broadband spectrum incident light through a composite transmittance curve generated through the coupling of an interlayer light field in a multi-layer optical resonant cavity optical filter, and obtaining the filtering light of spatial heterogeneous coding; the area array image sensor is used for receiving the filtered light subjected to spatial heterogeneous coding, and light signals of the filtered light are converted into original image data containing spectrum coding information; and determining a composite transmittance matrix corresponding to the composite transmittance curve, and performing spectrum reconstruction based on the original image data and the composite transmittance matrix to obtain target hyperspectral image data. The multi-peak, non-periodic and narrow-bandwidth composite transmittance curve is generated through the multi-layer optical resonant cavity optical filter, the number of spectrum channels is increased, a wide spectrum can be covered without switching the optical filter, the imaging efficiency is improved, meanwhile, the requirement for limited equidistance is met, and the reconstruction quality and reliability of spectrum data are ensured.

Description

Technical Field

[0001] This disclosure relates to the field of spectral imaging technology, and in particular to a multimode resonant photon computational spectral imaging method and architecture. Background Technology

[0002] Spectral imaging technology achieves three-dimensional information representation—two-dimensional spatial information and one-dimensional spectral information—by simultaneously capturing spatial images and continuous spectral information of a target, possessing irreplaceable application value in multiple fields. For example, in environmental monitoring, a broad spectral range covering the ultraviolet (300-400 nm) to near-infrared (760-1100 nm) is required to simultaneously identify trace volatile organic compounds (VOCs) in the atmosphere and chlorophyll concentrations in water bodies; in the biomedical field, this technology can capture subtle spectral differences between tumor tissue and normal tissue, providing crucial information for label-free pathological diagnosis; and in high-end industrial quality inspection, the detection of the composition and purity of semiconductor materials and precision optical components also requires a combination of broad spectral range and high resolution.

[0003] In spectral imaging systems, filter elements are the core components for spectral selection and resolution. Their performance directly determines the system's spectral resolution and luminous flux efficiency, thus affecting imaging speed and information accuracy. Specifically, Fabry-Perot resonator filters have become one of the commonly used filter components in computational spectral imaging systems due to their relatively simple structure, easily tunable passband center wavelength, customizable bandwidth, and low manufacturing cost. The basic principle of a Fabry-Perot resonator filter is based on a resonant cavity composed of two parallel highly reflective mirrors. Incident light undergoes multi-beam interference within the cavity, forming high transmittance peaks only near specific wavelengths that meet the resonance conditions, thereby achieving spectral selection.

[0004] However, in related technologies, filters based on single-layer Fabry-Perot resonant cavities, when applied to high-precision, high-efficiency computational spectral imaging applications, suffer from several drawbacks. The single-layer Fabry-Perot cavity forms a single-peak or periodic transmittance curve, allowing only 1-2 spectral channels to be acquired in a single imaging session. This necessitates multiple filter switching to cover a wide spectral range, resulting in low imaging efficiency and poor adaptability to dynamic scenes. Furthermore, the full width at half maximum (FWHM) of the transmission peak in a single-layer Fabry-Perot cavity filter is limited by the cavity's fineness, typically exceeding 15 nm, making it difficult to accurately distinguish target components with subtle spectral differences (such as different types of biological tissues or pollutants with similar chemical compositions), thus restricting detection accuracy. The measurement matrix constructed from a single or periodic simple transmittance curve generated by a single-layer Fabry-Perot cavity exhibits high coherence between column vectors, making it difficult to satisfy the RIP condition, leading to ill-conditioned characteristics in the measurement matrix. This reduces the reconstruction quality and reliability of spectral data. Additionally, periodic transmittance curves are prone to overlapping transmittance peaks of different wavelengths over a wide spectral range, causing spectral aliasing and reducing spectral identification accuracy. Therefore, there is an urgent need for a multimode resonant photonic computational spectral imaging method and architecture to achieve efficient and high-resolution spectral imaging and ensure the reconstruction quality and reliability of spectral data. Summary of the Invention

[0005] This disclosure aims to at least partially address one of the technical problems in the related art.

[0006] Therefore, the first objective of this disclosure is to propose a multimode resonant photonic computational spectral imaging method. By generating a composite transmittance curve with multiple peaks, non-periodicity, and narrow bandwidth through multi-layer optical resonant cavity filters, the number of spectral channels is increased, and a wide spectrum can be covered without switching filters, thereby improving imaging efficiency. The corresponding composite transmittance matrix is ​​determined by the transmittance of the multi-layer optical resonant cavity for different wavelengths of light, which is characterized by the composite transmittance curve. This satisfies the finite isometry requirement, reduces reconstruction error, and ensures the reconstruction quality and reliability of spectral data.

[0007] To achieve the above objectives, a first aspect of this disclosure proposes a multimode resonant photon computational spectral imaging method, comprising:

[0008] By using the composite transmittance curve generated by the interlayer optical field coupling in the multilayer optical resonant cavity filter, wavelength selection and intensity modulation of broadband incident light are performed to obtain spatially heterogeneously encoded filtered light. The spatially heterogeneously encoded filtered light is received using an area array image sensor, and the optical signal of the filtered light is converted into raw image data containing spectral encoding information; The composite transmittance matrix corresponding to the composite transmittance curve is determined, and spectral reconstruction is performed based on the original image data and the composite transmittance matrix to obtain the target hyperspectral image data.

[0009] Optionally, the multilayer optical resonant cavity filter includes at least two layers of Fabry-Perot resonant cavities stacked along the optical axis, and each layer of Fabry-Perot resonant cavity includes upper and lower mirrors composed of multiple dielectric thin films and a dielectric cavity layer between them, and a dielectric isolation layer is disposed between two adjacent layers of Fabry-Perot resonant cavities to regulate the interlayer coupling strength.

[0010] Optionally, the material of the dielectric cavity layer is determined based on the target spectral range.

[0011] Optionally, determining the material of the dielectric cavity layer based on the target spectral range includes: if the target spectral range is the visible light range, then the material of the dielectric cavity layer is SiO2 or TiO2; and / or, if the target spectral range is the near-infrared range, then the material of the dielectric cavity layer is Si3N4.

[0012] Optionally, spatial heterogeneous distribution of the composite transmittance curve can be achieved by introducing process tolerances during the fabrication of the multilayer optical resonant cavity filter.

[0013] Optionally, determining the composite transmittance matrix corresponding to the composite transmittance curve includes: The composite transmittance curve is preprocessed to obtain a standardized transmittance curve; For each spatial location index m and each discrete wavelength index n, the transmittance value at the corresponding wavelength is obtained from the normalized transmittance curve; The transmittance value is assigned to the element in the m-th row and n-th column of the composite transmittance matrix to obtain the composite transmittance matrix.

[0014] Optionally, the step of performing spectral reconstruction based on the original image data and the composite transmittance matrix to obtain target hyperspectral image data includes: obtaining target hyperspectral image data based on the original image data and the composite transmittance matrix through a spectral compressed sensing iterative solution algorithm, wherein the spectral compressed sensing iterative solution algorithm is any one of orthogonal matching pursuit (OMP), sparse adaptive matching pursuit (SAMP), alternating direction multiplier method (ADMM), or gradient projection sparse reconstruction (GPSR) algorithm.

[0015] Optionally, spectral reconstruction is performed based on the original image data and the composite transmittance matrix to obtain target hyperspectral image data, including: inputting the original image data and the composite transmittance matrix into a target spectral neural network model to obtain target hyperspectral image data, wherein the target spectral neural network model is an MST network or a SPECAT network.

[0016] To achieve the above objectives, a second aspect of this disclosure provides a multimode resonant photon computational spectral imaging device, the device comprising: The multilayer optical resonant cavity filter module is used to select the wavelength and modulate the intensity of broadband incident light by using the composite transmittance curve generated by the interlayer optical field coupling in the multilayer optical resonant cavity filter, so as to obtain the spatially heterogeneously encoded filtered light. The imaging detection module is used to receive the spatially heterogeneously encoded filtered light using an area array image sensor, and convert the optical signal of the filtered light into raw image data containing spectral encoding information; The photonic computing processing module is used to determine the composite transmittance matrix corresponding to the composite transmittance curve, and to perform spectral reconstruction based on the original image data and the composite transmittance matrix to obtain the target hyperspectral image data.

[0017] To achieve the above objectives, a third aspect of this disclosure proposes a multimode resonant photonic computational spectral imaging architecture, wherein the multimode resonant photonic computational spectral imaging architecture includes a multimode resonant photonic computational spectral imaging device.

[0018] In summary, the multimode resonant photonic computational spectral imaging method and architecture provided in this disclosure include: using a composite transmittance curve generated by interlayer optical field coupling in a multilayer optical resonant cavity filter to perform wavelength selection and intensity modulation on broadband incident light, obtaining spatially heterogeneously encoded filtered light; receiving the spatially heterogeneously encoded filtered light using an area array image sensor and converting the optical signal of the filtered light into raw image data containing spectral encoding information; determining the composite transmittance matrix corresponding to the composite transmittance curve; and performing spectral reconstruction based on the raw image data and the composite transmittance matrix to obtain the target hyperspectral image data. This disclosure generates a multi-peaked, non-periodic, and narrow-bandwidth composite transmittance curve using a multilayer optical resonant cavity filter, increasing the number of spectral channels and covering a wide spectrum without switching filters, thus improving imaging efficiency; determining the corresponding composite transmittance matrix by using the transmittance of the multilayer optical resonant cavity for different wavelengths of light represented by the composite transmittance curve satisfies the finite isometry requirement, reduces reconstruction errors, and ensures the reconstruction quality and reliability of the spectral data.

[0019] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which: Figure 1 This is a schematic flowchart of a multimode resonant photonic computational spectral imaging method provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of a multilayer optical resonant cavity filter provided in an embodiment of this disclosure; Figure 3 This is a schematic diagram of the structure of a multimode resonant photonic computational spectral imaging device provided in an embodiment of this disclosure. Detailed Implementation

[0021] Embodiments of this disclosure are described in detail below. Examples of these embodiments are illustrated 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 disclosure, and should not be construed as limiting this disclosure.

[0022] The multimode resonant photonic computational spectral imaging method and architecture of this disclosure will be described in detail below with reference to specific embodiments.

[0023] Figure 1 This is a schematic flowchart of a multimode resonance photonic computational spectral imaging method proposed in an embodiment of this disclosure. Wherein, as... Figure 1 As shown, the above-mentioned multimode resonant photon computational spectral imaging method includes the following steps: Step 101: By using the composite transmittance curve generated by the interlayer optical field coupling in the multilayer optical resonant cavity filter, wavelength selection and intensity modulation are performed on the broadband incident light to obtain spatially heterogeneously encoded filtered light.

[0024] In one embodiment of this disclosure, the multilayer optical resonant cavity filter may include at least two layers of Fabry-Perot resonant cavities stacked along the optical axis, and each layer of Fabry-Perot resonant cavity includes upper and lower mirrors composed of multiple dielectric thin films and a dielectric cavity layer between them, and a dielectric isolation layer is disposed between two adjacent layers of Fabry-Perot resonant cavities for adjusting the interlayer coupling strength.

[0025] In one embodiment of this disclosure, the thickness of the dielectric isolation layer can be set within a preset range as needed, wherein the preset range can be (10nm, 100nm). Also, in one embodiment of this disclosure, the material of the dielectric isolation layer can be SiO2.

[0026] In one embodiment of this disclosure, the material of the dielectric cavity layer can be determined based on the target spectral range. Specifically, in one embodiment of this disclosure, determining the material of the dielectric cavity layer based on the target spectral range includes: if the target spectral range is the visible light range, then the material of the dielectric cavity layer is SiO2 or TiO2; and / or, if the target spectral range is the near-infrared range, then the material of the dielectric cavity layer is Si3N4.

[0027] In one embodiment of this disclosure, spatial heterogeneous distribution of composite transmittance curves can be achieved by introducing process tolerances during the fabrication of multilayer optical resonant cavity filters.

[0028] For example, in one embodiment of this disclosure, Figure 2 This is a schematic diagram of a multilayer optical resonant cavity filter according to an embodiment of this disclosure. Figure 2 As shown, the multilayer optical resonant cavity filter includes three Fabry-Perot resonant cavities. From top to bottom, they are: a top substrate (SiO2) serving as the top support of the device and providing low-refractive-index optical isolation; a first dielectric isolation layer (SiO2) isolating the upper substrate from the first Fabry-Perot resonant cavity, providing a dielectric space for interlayer optical field coupling; the first Fabry-Perot resonant cavity (lithium niobate (LiNbO3) substrate), with a thickness of 0.57 nm, determines the resonant wavelength of this first Fabry-Perot resonant cavity; a second dielectric isolation layer (SiO2) isolating the first Fabry-Perot resonant cavity from the second Fabry-Perot resonant cavity, enabling optical field coupling between sub-cavities; and the second Fabry-Perot resonant cavity (lithium niobate (LiNbO3) substrate). The first Fabry-Perot (LiNbO3) substrate is 2.25 nm thick and has a different resonant wavelength than the first Fabry-Perot resonator. The second dielectric isolation layer (SiO2) isolates the second and third Fabry-Perot resonators, further enhancing the interlayer coupling effect. The third Fabry-Perot resonator (LiNbO3) substrate is 0.34 nm thick and has a different resonant wavelength than both the first and second Fabry-Perot resonators. The third dielectric isolation layer (SiO2) isolates the third Fabry-Perot resonator from the bottom substrate, completing the optical isolation of the overall structure. The bottom substrate (SiO2) serves as the underlying support for the device, ensuring structural stability.

[0029] In one embodiment of this disclosure, the optical field coupling effect between the cavities in a multilayer optical resonant cavity filter is utilized to break the single-peak / periodic transmittance characteristics of a traditional single-layer Fabry-Perot cavity, generating a composite transmittance curve with multiple peaks, aperiodicity, and narrow bandwidth. Specifically, in one embodiment of this disclosure, interlayer coupling causes the resonant modes of each cavity layer to interact, forming multiple independent oscillation periods (i.e., "multimode resonance"), which manifests as a linear combination of multiple frequency peaks in the Fourier domain. Furthermore, the aperiodic characteristic avoids aliasing within a certain spectral range, preventing the indistinguishability of multiple wavelength components; the coupling effect can compress the full width at half maximum (FWHM) of a single transmission peak, improving spectral resolution.

[0030] Step 102: Receive spatially heterogeneously encoded filtered light using an area array image sensor, and convert the light signal of the filtered light into raw image data containing spectral encoding information.

[0031] In one embodiment of this disclosure, after obtaining spatially heterogeneously encoded filtered light through the above steps, the spatially heterogeneously encoded filtered light can be received using an area array image sensor, and the optical signal of the filtered light can be converted into raw image data containing spectral encoding information.

[0032] Step 103: Determine the composite transmittance matrix corresponding to the composite transmittance curve, and perform spectral reconstruction based on the original image data and the composite transmittance matrix to obtain the target hyperspectral image data.

[0033] In one embodiment of this disclosure, after obtaining the composite transmittance curve through the above steps, the composite transmittance matrix corresponding to the composite transmittance curve can be determined.

[0034] In one embodiment of this disclosure, the method for determining the composite transmittance matrix corresponding to the composite transmittance curve may include the following steps: Step 1031: Preprocess the composite transmittance curve to obtain a standardized transmittance curve; Step 1032: For each spatial location index m and each discrete wavelength index n, obtain the transmittance value at the corresponding wavelength from the normalized transmittance curve. Step 1033: Assign the transmittance value to the element in the m-th row and n-th column of the composite transmittance matrix to obtain the composite transmittance matrix.

[0035] In one embodiment of this disclosure, the composite transmittance curve can be preprocessed by denoising, smoothing, normalization and baseline correction in sequence to obtain a standardized transmittance curve.

[0036] In one embodiment of this disclosure, for each spatial location index m and each discrete wavelength index n, the transmittance value at the corresponding wavelength is obtained from the normalized transmittance curve, and the transmittance value is assigned to the element in the m-th row and n-th column of the composite transmittance matrix to obtain the composite transmittance matrix.

[0037] In one embodiment of this disclosure, the composite transmittance curve characterizes the transmittance of the multilayer optical resonant cavity for light waves of different wavelengths. It can be used as a measurement matrix for compressed sensing calculation, satisfying the requirement of finite isometry, making the calculation process well-balanced, and reducing reconstruction errors.

[0038] In one embodiment of this disclosure, the method for obtaining target hyperspectral image data by spectral reconstruction based on the original image data and the composite transmittance matrix may include: obtaining target hyperspectral image data by using a spectral compressed sensing iterative solution algorithm based on the original image data and the composite transmittance matrix, wherein the spectral compressed sensing iterative solution algorithm is any one of the following: Orthogonal Matching Pursuit (OMP), Sparse Adaptive Matching Pursuit (SAMP), Alternating Direction Multiplier Method (ADMM), or Gradient Projection Sparse Reconstruction (GPSR) algorithm.

[0039] In one embodiment of this disclosure, the method for obtaining target hyperspectral image data by spectral reconstruction based on the original image data and the composite transmittance matrix may include: inputting the original image data and the composite transmittance matrix into a target spectral neural network model to obtain the target hyperspectral image data. The target spectral neural network model may be an MST network or a SPECAT network.

[0040] The multimode resonant photonic computational spectral imaging method disclosed herein includes: using a composite transmittance curve generated by interlayer optical field coupling in a multilayer optical resonant cavity filter to perform wavelength selection and intensity modulation on broadband incident light, obtaining spatially heterogeneously encoded filtered light; receiving the spatially heterogeneously encoded filtered light using an area array image sensor, and converting the optical signal of the filtered light into raw image data containing spectral encoding information; determining the composite transmittance matrix corresponding to the composite transmittance curve, and performing spectral reconstruction based on the raw image data and the composite transmittance matrix to obtain target hyperspectral image data. This disclosure generates a multi-peaked, non-periodic, and narrow-bandwidth composite transmittance curve using a multilayer optical resonant cavity filter, increasing the number of spectral channels and covering a wide spectrum without switching filters, thus improving imaging efficiency; determining the corresponding composite transmittance matrix by using the transmittance of the multilayer optical resonant cavity for different wavelengths of light characterized by the composite transmittance curve satisfies the finite isometry requirement, reduces reconstruction errors, and ensures the reconstruction quality and reliability of the spectral data.

[0041] Figure 3 This disclosure provides a multimode resonant photonic computational spectral imaging device. For example... Figure 3 As shown, the multimode resonant photonic computational spectral imaging device may include a multilayer optical resonant cavity filtering module 301, an imaging detection module 302, and a photonic computational processing module 303, wherein, The multilayer optical resonant cavity filter module 301 is used to perform wavelength selection and intensity modulation on broadband incident light through the composite transmittance curve generated by the interlayer optical field coupling in the multilayer optical resonant cavity filter, so as to obtain spatially heterogeneous encoded filtered light. Imaging detection module 302 is used to receive spatially heterogeneously encoded filtered light using an area array image sensor and convert the light signal of the filtered light into raw image data containing spectral encoding information; The photonic computing processing module 303 is used to determine the composite transmittance matrix corresponding to the composite transmittance curve, and to perform spectral reconstruction based on the original image data and the composite transmittance matrix to obtain the target hyperspectral image data.

[0042] In one embodiment of this disclosure, the multilayer optical resonant cavity filter includes at least two layers of Fabry-Perot resonant cavities stacked along the optical axis, and each layer of Fabry-Perot resonant cavity includes upper and lower mirrors composed of multiple dielectric thin films and a dielectric cavity layer between them, and a dielectric isolation layer is disposed between two adjacent layers of Fabry-Perot resonant cavities for adjusting the interlayer coupling strength.

[0043] In one embodiment of this disclosure, the material of the dielectric cavity layer is determined based on the target spectral range.

[0044] In one embodiment of this disclosure, the photonic computing processing module 303 is specifically used for: The composite transmittance curve is preprocessed to obtain a standardized transmittance curve; For each spatial location index m and each discrete wavelength index n, the transmittance value at the corresponding wavelength is obtained from the normalized transmittance curve; The transmittance value is assigned to the element in the m-th row and n-th column of the composite transmittance matrix to obtain the composite transmittance matrix.

[0045] In one embodiment of this disclosure, the photonic computing processing module 303 is further configured to: obtain target hyperspectral image data based on the original image data and the composite transmittance matrix using a spectral compressed sensing iterative solution algorithm, wherein the spectral compressed sensing iterative solution algorithm is any one of orthogonal matching pursuit (OMP), sparse adaptive matching pursuit (SAMP), alternating direction multiplier method (ADMM), or gradient projection sparse reconstruction (GPSR) algorithm.

[0046] In another embodiment of this disclosure, the photonic computing processing module 303 is further configured to: input the original image data and the composite transmittance matrix into the target spectral neural network model to obtain target hyperspectral image data, wherein the target spectral neural network model is an MST network or a SPECAT network.

[0047] The multimode resonant photonic computational spectral imaging device disclosed herein includes a multilayer optical resonator filtering module, an imaging detection module, and a photonic computational processing module. The multilayer optical resonator filtering module is used to perform wavelength selection and intensity modulation on broadband incident light using the composite transmittance curve generated by interlayer optical field coupling in the multilayer optical resonator filter, obtaining spatially heterogeneously encoded filtered light. The imaging detection module is used to receive the spatially heterogeneously encoded filtered light using an area array image sensor and convert the optical signal of the filtered light into raw image data containing spectral encoding information. The photonic computational processing module is used to determine the composite transmittance matrix corresponding to the composite transmittance curve and perform spectral reconstruction based on the raw image data and the composite transmittance matrix to obtain target hyperspectral image data. This disclosure generates a composite transmittance curve with multiple peaks, non-periodicity, and narrow bandwidth by using a multi-layer optical resonant cavity filter, which increases the number of spectral channels and can cover a wide spectrum without switching filters, thereby improving imaging efficiency. The composite transmittance matrix is ​​determined by the transmittance of the multi-layer optical resonant cavity for different wavelengths of light, which satisfies the requirement of finite isometry, reduces reconstruction error, and ensures the reconstruction quality and reliability of spectral data.

[0048] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this disclosure all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0049] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0050] This disclosure is intended to provide implementation schemes for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0051] The acquisition, transmission, storage, use, and processing of data in this disclosed technical solution all comply with the relevant provisions of national laws and regulations.

[0052] It should be noted that in the embodiments disclosed herein, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary and are intended only to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used such solutions.

[0053] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this disclosure. 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.

[0054] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0055] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure 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 will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0056] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0057] It should be understood that various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in 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 (PGAs), field-programmable gate arrays (FPGAs), etc.

[0058] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0059] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0060] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A multimode resonant photon computational spectral imaging method, characterized in that, include: By using the composite transmittance curve generated by the interlayer optical field coupling in the multilayer optical resonant cavity filter, wavelength selection and intensity modulation of broadband incident light are performed to obtain spatially heterogeneously encoded filtered light. The spatially heterogeneously encoded filtered light is received using an area array image sensor, and the optical signal of the filtered light is converted into raw image data containing spectral encoding information; The composite transmittance matrix corresponding to the composite transmittance curve is determined, and spectral reconstruction is performed based on the original image data and the composite transmittance matrix to obtain the target hyperspectral image data.

2. The method according to claim 1, characterized in that, The multilayer optical resonant cavity filter includes at least two layers of Fabry-Perot resonant cavities stacked along the optical axis. Each Fabry-Perot resonant cavity includes upper and lower mirrors composed of multiple dielectric thin films and a dielectric cavity layer between them. A dielectric isolation layer is provided between two adjacent Fabry-Perot resonant cavities to regulate the interlayer coupling strength.

3. The method according to claim 2, characterized in that, The material of the dielectric cavity layer is determined based on the target spectral range.

4. The method according to claim 3, characterized in that, The step of determining the material of the dielectric cavity layer according to the target spectral range includes: if the target spectral range is the visible light range, then the material of the dielectric cavity layer is SiO2 or TiO2; and / or, if the target spectral range is the near-infrared range, then the material of the dielectric cavity layer is Si3N4.

5. The method according to claim 2, characterized in that, By introducing process tolerances during the fabrication of the multilayer optical resonant cavity filter, the spatial heterogeneous distribution of the composite transmittance curve is achieved.

6. The method according to claim 1, characterized in that, Determining the composite transmittance matrix corresponding to the composite transmittance curve includes: The composite transmittance curve is preprocessed to obtain a standardized transmittance curve; For each spatial location index m and each discrete wavelength index n, the transmittance value at the corresponding wavelength is obtained from the normalized transmittance curve; The transmittance value is assigned to the element in the m-th row and n-th column of the composite transmittance matrix to obtain the composite transmittance matrix.

7. The method according to claim 1, characterized in that, The step of performing spectral reconstruction based on the original image data and the composite transmittance matrix to obtain target hyperspectral image data includes: obtaining target hyperspectral image data based on the original image data and the composite transmittance matrix through a spectral compressed sensing iterative solution algorithm, wherein the spectral compressed sensing iterative solution algorithm is any one of orthogonal matching pursuit (OMP), sparse adaptive matching pursuit (SAMP), alternating direction multiplier method (ADMM), or gradient projection sparse reconstruction (GPSR) algorithm.

8. The method according to claim 1, characterized in that, The step of performing spectral reconstruction based on the original image data and the composite transmittance matrix to obtain target hyperspectral image data includes: inputting the original image data and the composite transmittance matrix into a target spectral neural network model to obtain target hyperspectral image data, wherein the target spectral neural network model is an MST network or a SPECAT network.

9. A multimode resonant photon computational spectral imaging device, the device comprising: The multilayer optical resonant cavity filter module is used to select the wavelength and modulate the intensity of broadband incident light by using the composite transmittance curve generated by the interlayer optical field coupling in the multilayer optical resonant cavity filter, so as to obtain the spatially heterogeneously encoded filtered light. The imaging detection module is used to receive the spatially heterogeneously encoded filtered light using an area array image sensor, and convert the optical signal of the filtered light into raw image data containing spectral encoding information; The photon computing processing module is used to determine the composite transmittance matrix corresponding to the composite transmittance curve, and to perform spectral reconstruction based on the original image data and the composite transmittance matrix to obtain the target hyperspectral image data.

10. A multimode resonant photonic computational spectral imaging architecture, wherein, The multimode resonant photonic computational spectral imaging architecture includes a multimode resonant photonic computational spectral imaging device.

Images