Design method and apparatus of a spectral imaging system, spectral imaging system and use thereof, electronic device

Abstract

This disclosure relates to a design method and apparatus for a spectral imaging system, the spectral imaging system and its application, and electronic equipment. The method includes obtaining first response functions of multiple first selectable filters and second response functions of multiple second selectable filters; calculating the total response function of each dual-layer selectable filter structure using each first response function and each second response function; performing permutational orthogonal triangular decomposition on all total response functions to obtain a first result sorted by the correlation strength of all total response functions; and selecting multiple target response functions from the first result to form the target filter structure used by the spectral imaging system. The designed spectral imaging system can be used for spectral imaging or spatial spectral information acquisition. This disclosure can determine an ideal dual-layer filter structure, optimize the imaging performance of the spectral imaging system from the perspective of hardware structure, and reduce the error of the reconstructed image.

Description

Technical Field

[0001] This disclosure relates to the field of spectral imaging technology, and in particular to a design method and apparatus for a spectral imaging system, a spectral imaging system and its applications, and electronic equipment. Background Technology

[0002] Spectroscopic technology and spectral sensing technology are important means of exploring nature. Spectral imaging technology combines imaging technology and spectral sensing technology to simultaneously acquire spatial and spectral information of matter. Acquiring high-dimensional spatial spectral information can play a more important role in fields such as medicine, remote sensing, food, and agriculture.

[0003] However, there is no perfect spectral imaging system based on spectral imaging technology in theory or in practice, which leads to certain errors in the spectral imaging results and thus affects downstream issues of spectral sensing to varying degrees. Summary of the Invention

[0004] In view of this, this disclosure proposes a design method and apparatus for a spectral imaging system, a spectral imaging system and its application, and an electronic device, which can determine an ideal dual-layer filter structure, optimize the imaging performance of the spectral imaging system from the perspective of hardware structure, and reduce the error of the reconstructed image.

[0005] According to one aspect of this disclosure, a design method for a spectral imaging system is provided, comprising: acquiring a plurality of different first optional filters and a plurality of different second optional filters, wherein each first response function represents the relative transmittance of the first optional filter at different light wavelengths, and each second response function represents the relative transmittance of the second optional filter at different light wavelengths; calculating a total response function for each dual-layer optional filter structure using each first response function and each second response function, wherein each dual-layer optional filter structure includes a first optional filter and a second optional filter, and at least some of the first optional filters and / or second optional filters of the dual-layer optional filter structures are different, and each total response function represents the relative transmittance of the dual-layer optional filter structure at different light wavelengths; performing permutation orthogonal triangular decomposition on all total response functions to obtain a first result in which all total response functions are sorted according to the degree of correlation, and selecting a plurality of target response functions from the first result according to a preset selection rule, so as to form the target filter structure used by the spectral imaging system based on the dual-layer optional filter structure corresponding to each target response function.

[0006] In one possible implementation, each of the first optional filters is a quantum dot filter; and / or, the plurality of different second optional filters include a red filter, a green filter, and a blue filter.

[0007] In one possible implementation, the method further includes: performing permutational orthogonal triangular decomposition on the candidate response functions of all candidate filters in a preset database to obtain a second result in which all candidate response functions are sorted according to the strength of their correlation, wherein each candidate response function represents the relative transmittance of the candidate filter corresponding to different light wavelengths; if the second result is sorted from strong to weak correlation, the candidate filter corresponding to the first number of candidate response functions ranked last in the second result is selected as the first selectable filter; if the second result is sorted from weak to strong correlation, the candidate filter corresponding to the first number of candidate response functions ranked first in the second result is selected as the first selectable filter.

[0008] In one possible implementation, each of the total response functions is obtained as the product of the first response function of the first optional filter and the second response function of the second optional filter in the corresponding dual-layer optional filter structure.

[0009] In one possible implementation, multiple target response functions are selected from the first results according to a preset selection rule, including: if the first results are sorted from strong to weak correlation, the second number of total response functions ranked last in the first results are selected as the target response functions; if the first results are sorted from weak to strong correlation, the second number of total response functions ranked first in the first results are selected as the target response functions.

[0010] In one possible implementation, the method further includes: removing target response functions whose relative transmittance peak value is less than a preset threshold from the plurality of target response functions.

[0011] In one possible implementation, the method further includes: arranging the relative positions of the dual-layer optional filter structures corresponding to each target response function according to a preset arrangement to obtain the target filter structure.

[0012] According to another aspect of this disclosure, a design apparatus for a spectral imaging system is provided, comprising: an acquisition module, configured to acquire first response functions of a plurality of different first selectable filters and second response functions of a plurality of different second selectable filters, wherein each first response function represents the relative transmittance of the first selectable filter at different light wavelengths, and each second response function represents the relative transmittance of the second selectable filter at different light wavelengths; and a calculation module, configured to perform calculations using each first response function and each second response function to obtain a total response function for each dual-layer selectable filter structure, wherein each dual-layer selectable filter structure includes... The system includes a first optional filter and a second optional filter, wherein at least some of the first optional filter and / or second optional filter of the dual-layer optional filter structure are different, and each total response function represents the relative transmittance corresponding to the dual-layer optional filter structure at different light wavelengths; a selection module is used to perform permutation orthogonal triangular decomposition on all total response functions to obtain a first result in which all total response functions are sorted according to the degree of correlation, and selects multiple target response functions from the first result according to a preset selection rule, so as to form the target filter structure used by the spectral imaging system based on the dual-layer optional filter structure corresponding to each target response function.

[0013] In one possible implementation, each of the first optional filters is a quantum dot filter; and / or, the plurality of different second optional filters include a red filter, a green filter, and a blue filter.

[0014] In one possible implementation, the device further includes a decomposition module, configured to: perform permutational orthogonal triangular decomposition on the candidate response functions of all candidate filters in a preset database to obtain a second result in which all candidate response functions are sorted according to the strength of their correlation, wherein each candidate response function represents the relative transmittance of the candidate filter corresponding to a different light wavelength; if the second result is sorted from strong to weak correlation, the candidate filter corresponding to the first number of candidate response functions ranked last in the second result is selected as the first selectable filter; if the second result is sorted from weak to strong correlation, the candidate filter corresponding to the first number of candidate response functions ranked first in the second result is selected as the first selectable filter.

[0015] In one possible implementation, each of the total response functions is obtained as the product of the first response function of the first optional filter and the second response function of the second optional filter in the corresponding dual-layer optional filter structure.

[0016] In one possible implementation, multiple target response functions are selected from the first results according to a preset selection rule, including: if the first results are sorted from strong to weak correlation, the second number of total response functions ranked last in the first results are selected as the target response functions; if the first results are sorted from weak to strong correlation, the second number of total response functions ranked first in the first results are selected as the target response functions.

[0017] In one possible implementation, the device further includes a removal module for removing target response functions whose relative transmittance peak value is less than a preset threshold from the plurality of target response functions.

[0018] In one possible implementation, the device further includes an arrangement module for: arranging the relative positions of the dual-layer selectable filter structures corresponding to each target response function according to a preset arrangement method to obtain the target filter structure.

[0019] According to another aspect of this disclosure, a spectral imaging system is provided, wherein the target filter structure required by the system is obtained using the method described above.

[0020] According to another aspect of this disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-described method when executing instructions stored in the memory.

[0021] According to another aspect of this disclosure, a spectral imaging system obtained by the above method is provided for use in spectral imaging or spatial spectral information acquisition.

[0022] According to another aspect of this disclosure, a non-volatile computer-readable storage medium is provided that stores computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the above-described method.

[0023] According to another aspect of this disclosure, a computer program product is provided, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0024] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0025] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0026] Figure 1 A flowchart illustrating a design method for a spectral imaging system according to an embodiment of the present disclosure is shown.

[0027] Figures 2 to 4 A schematic diagram illustrating a design method for a spectral imaging system according to an embodiment of the present disclosure is shown.

[0028] Figure 5 This diagram illustrates the comparison of loss functions during the training process under different noise levels.

[0029] Figure 6 A block diagram of a design apparatus for a spectral imaging system according to an embodiment of the present disclosure is shown. Detailed Implementation

[0030] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0031] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0032] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0033] To facilitate understanding of the technical solutions provided by the embodiments of this disclosure by those skilled in the art, the technical environment for implementing the technical solutions will be described below.

[0034] Snapshot-based spectral imaging systems offer numerous advantages over traditional pushbroom-based systems, including faster imaging speeds and smaller system size, making them particularly advantageous in many scenarios. Snapshot-based systems capture a target with a single exposure and then reconstruct a multi-channel spectral image of the target in a specific wavelength band using a neural network, which is then used for subsequent downstream analysis. In other words, the system's input is a single-exposure snapshot image, and its output is a multi-spectral image in a specific wavelength band. Therefore, snapshot-based spectral imaging systems essentially fall within the research scope of computational imaging technology. This means that, both theoretically and practically, a perfect spectral image reconstruction network does not exist. Consequently, the imaging results and reconstructed images from snapshot-based systems contain certain errors, which affect downstream problems in spectral sensing to varying degrees. Therefore, designing and optimizing spectral imaging systems is of great significance.

[0035] To address the aforementioned technical problems, this disclosure provides a design method for a spectral imaging system, which optimizes the design scheme of the filter array in the spectral imaging system from a hardware system perspective, thereby fundamentally improving the performance of the imaging system.

[0036] Now combined Figures 1 to 5 The design method of the spectral imaging system provided in the embodiments of this disclosure is illustrated.

[0037] like Figure 1 As shown, the design method of this spectral imaging system may include the following steps S101 to S103.

[0038] Step S101: Obtain the first response function of multiple different first optional filters and the second response function of multiple different second optional filters.

[0039] Each first response function represents the relative transmittance of the first selectable filter at different light wavelengths. Each second response function represents the relative transmittance of the second selectable filter at different light wavelengths. Both the first and second response functions are essentially filter response functions, and their response functions can be reflected by transmission curves, for example... Figure 2 The left-hand diagram shows the transmission curves of quantum dot filters. Each of the first optional filters is a quantum dot filter, and / or each of the second optional filters can be a bandpass filter, such as multiple different second optional filters including a red (R) filter, a green (G) filter, and a blue (B) filter. Of course, other types of filters can also be used for the first and second optional filters, and the specific selection can be flexible according to actual needs.

[0040] In this embodiment, each of the first optional filters is a quantum dot filter, and the bandpass filter selected as the second optional filter can be a narrowband filter among bandpass filters. For example, the narrowband filter may include a red narrowband filter, a blue narrowband filter, and a green narrowband filter (hereinafter referred to as RGB narrowband filters). The quantum dot material used in the quantum dot filter has highly continuously tunable absorption spectral characteristics over a wide wavelength range from deep violet to mid-infrared. Figure 2 As shown in the left figure, the transmission curves of quantum dot filters are a cluster of curves with similar shapes, exhibiting both general high-pass characteristics and flexible tunability. The high-pass characteristic results in high luminous flux for quantum dot filters, but the similar transmission curves lead to high correlation between different quantum dot filters, preventing the maximum extraction of information from different wavelength bands. In contrast, RGB narrowband filters have lower luminous flux and a fixed curve shape, but their transmission curves show better correlation, allowing for the extraction of information from corresponding characteristic wavelength bands. This embodiment combines quantum dot filters and RGB narrowband filters into a multispectral filter array (hereinafter referred to as the filter array) with a dual-layer structure. This optimizes the hardware structure, improving the imaging performance of the quantum dot snapshot spectral imaging system and ultimately reducing errors in the reconstructed image results. Thus, the spectral imaging system designed based on a filter array using quantum dot materials has advantages such as low cost, lightweight portability, real-time operation, and stability, and has broad application prospects in many fields. In addition, in some embodiments, the RGB narrowband filter can be directly adopted from the Bayer array RGB narrowband filter, which is widely used in related technologies, thus effectively reducing design complexity.

[0041] There are numerous quantum dot filters available, each with a different response function, resulting in a vast array of possible response functions. However, using the response functions of all quantum dot filters to select the one that meets the requirements for designing a dual-layer filter structure would incur a very high computational load. Therefore, this embodiment of the present disclosure can first select a subset of response functions from a pre-set database, reducing computational load while determining the first selectable filter. The pre-set database can store relevant parameters for different quantum dot filters (i.e., candidate filters), including the response function of the corresponding quantum dot filter (i.e., the candidate response function), the quantum dot material used, the manufacturer, etc. The design method may further include: performing permutational orthogonal triangular decomposition (hereinafter referred to as QR decomposition) on the candidate response functions of all candidate filters in the preset database to obtain a second result in which all candidate response functions are sorted according to the strength of their correlation. Each candidate response function represents the relative transmittance of the candidate filter at different light wavelengths. Strong correlation between multiple candidate response functions indicates that they are similar; weak correlation indicates that they are dissimilar; and complete lack of correlation indicates that they are orthogonal. If the second result is sorted from strongest to weakest correlation, the candidate filter corresponding to the first number of candidate response functions ranked last in the second result is selected as the first selectable filter. If the second result is sorted from weakest to strongest correlation, the candidate filter corresponding to the first number of candidate response functions ranked first in the second result is selected as the first selectable filter. The first quantity can be determined based on the number of dual-layer selectable filter structures in the periodically arranged target filter structure in the filter array. For example, if the design requirement is that the target filter structure is formed by a 3-row * 3-column arrangement of dual-layer selectable filter structures, that is, 9 dual-layer selectable filter structures are required, then the first quantity can be directly set to 9. Therefore, in this embodiment, the response functions corresponding to the 9 least relevant transmission curves can be selected from the preset database as the first response function used for calculation in the subsequent step S102. Figure 2 As shown in the right figure, nine transmission curves with strong orthogonality were selected using QR decomposition. In other words, QR decomposition can be used to select the top few least relevant channels. Here, a channel can be understood as the light band corresponding to the higher relative transmittance in the transmission curve. By using these least relevant channels, more light bands can be covered, thereby maximizing the extraction of information from different bands and improving the performance of the spectral imaging system.

[0042] Step S102: Calculate the total response function of each double-layer selectable filter structure using each first response function and each second response function.

[0043] Each dual-layer selectable filter structure includes a first selectable filter and a second selectable filter. At least some of the dual-layer selectable filter structures have different first and / or second selectable filters. The total response function represents the relative transmittance of the dual-layer selectable filter structure at different light wavelengths. Similarly to the first response function, the total response function is essentially the response function of a combined filter (i.e., a dual-layer selectable filter structure), and can also be reflected by a transmittance curve, for example... Figure 3 The transmission curve of the dual-layer selectable filter structure is shown in Figure c. Each total response function is obtained based on the product of the first response function of the first selectable filter and the second response function of the second selectable filter in the corresponding dual-layer selectable filter structure. For example, if the first response function of a first selectable filter T is T(λ), and the second response function of a second selectable filter R is R(λ), then the total response function of the dual-layer selectable filter structure implemented using filters T and R is T(λ)R(λ). In this embodiment, the first response function includes... Figure 3 The response functions corresponding to the nine weakly correlated transmission curves shown in Figure a, each second response function includes... Figure 3 The response functions corresponding to the three transmission curves of the RGB narrowband filter shown in Figure b can be obtained by multiplying the results of these nine first response functions and three second response functions. Figure 3 The 27 transmission curves shown in Figure c represent the total response functions of 27 dual-layer selectable filter structures. These can be used to select multiple target response functions in step S103. It should be noted that for the QR decomposition in step S103 to be meaningful, a reasonable first quantity should be set based on the number of dual-layer selectable filter structures in the target filter structure required by the design. This first quantity sets the number of first response functions (i.e., the number of first selectable filters), and a reasonable number of second response functions (i.e., the number of second selectable filters) should also be set. Ideally, the total number of response functions should be greater than the number of dual-layer selectable filter structures required to form the target filter structure required by the design. For example, if 9 dual-layer selectable filter structures are needed, and the number of second response functions is set to 3, then it is best to set the first quantity to 4, thus obtaining 12 total response functions for dual-layer selectable filter structures. This allows step S103 to select 9 target response functions from these 12 total response functions through QR decomposition.

[0044] Step S103: Perform orthogonal triangular decomposition on all total response functions to obtain a first result in which all total response functions are sorted according to the degree of correlation. Then, select multiple target response functions from the first result according to the preset selection rules, and form the target filter structure used by the spectral imaging system based on the dual-layer selectable filter structure corresponding to each target response function.

[0045] Selecting multiple target response functions according to a preset selection rule is essentially about choosing a suitable dual-layer selectable filter structure (i.e., the matching relationship between the first selectable filter and the second selectable filter) from multiple dual-layer selectable filter structures to form a target filter structure. In some embodiments, step S103, selecting multiple target response functions from the first result according to the preset selection rule, may include: if the first result is sorted from strongest to weakest correlation, using the second-ranked total response function in the first result as the target response function; if the first result is sorted from weakest to strongest correlation, using the second-ranked total response function in the first result as the target response function.

[0046] The second quantity can also be determined based on the number of dual-layer selectable filter structures in the periodically arranged target filter structure in the filter array. For example, if the design requirement is to form a target filter structure using a 3x3 arrangement of dual-layer selectable filter structures, i.e., requiring 9 dual-layer selectable filter structures, then the second quantity can be directly set to 9. In this way, the target filter structure can be directly formed based on the dual-layer selectable filter structures corresponding to the first 9 total response functions. Alternatively, the second quantity can be set to 6. In this way, one or more of the dual-layer selectable filter structures corresponding to the first 6 total response functions can be repeatedly set in the 3x3 target filter structure to form the desired target filter structure. Alternatively, the second quantity can be set to 12. If the second quantity is greater than the actual number of dual-layer selectable filter structures required to form the target filter structure, a secondary screening can be performed on the multiple target response functions selected by QR decomposition before arranging them to form the target filter structure (see below for details). Nine more total response functions can be selected from these 12 total response functions to form the desired target filter structure. It should be noted that a second filtering can be performed regardless of the second quantity set, and the timing of the second filtering can be flexibly set according to actual needs.

[0047] The design method may further include: after selecting multiple target response functions from the first result, removing target response functions whose relative transmittance peak value is less than a preset threshold, thereby forming a target filter structure based on the dual-layer selectable filter structure corresponding to the target response functions after secondary screening. The preset threshold can be flexibly set according to actual design requirements, which may result in the number of target response functions after secondary screening (hereinafter referred to as the number A) not being equal to the actual number of dual-layer selectable filter structures required to form the target filter structure (hereinafter referred to as the number B). If the number A is greater than the number B, the top B target response functions with higher relative transmittance peak values ​​can be selected from the number A to form the target filter structure; if the number A is less than the number B, the dual-layer selectable filter structure corresponding to one or more target response functions can be repeatedly set in the target filter structure.

[0048] Once the target response function is determined, it becomes clear which first selectable filter and which second selectable filter constitute the dual-layer selectable filter structure. For example... Figure 4 As shown, the QR decomposition filtering function is used to select nine target response functions from all total response functions. The corresponding dual-layer selectable filter structures can be used to form the target filter structure. Thus, QR decomposition can quickly obtain a relatively good matching relationship between the first selectable filter and the second selectable filter. In this embodiment, if the first selectable filter is a quantum dot filter and the second selectable filter is an RGB narrowband filter, then... Figure 4 As shown, based on the nine selected target response functions, it can be determined which of the following filters—R-narrowband filter, G-narrowband filter, or B-narrowband filter—is matched after each quantum dot filter, thus obtaining the desired target filter structure. In this way, matching a fixed R / G / B filter after the quantum dot filter ensures spatial resolution and enables customization of the dual-layer filter structure. In some embodiments, the design method may further include: arranging the relative positions of the dual-layer selectable filter structures corresponding to each target response function according to a preset arrangement to obtain the target filter structure, providing greater design flexibility.

[0049] To verify the effectiveness of the design method of the spectral imaging system provided in this disclosure, an imaging-reconstruction simulation process was performed on a publicly available multispectral image dataset. The target wavelength range was 400nm-700nm, with 31 wavelengths and a 10nm step size. The reconstruction network selected was the Alternating Directional Multiplier Method (ADMM-net). Comparisons were made from several aspects, including the loss function during training, the reconstruction error on the test set, and the visualization of the reconstructed spectra. Figure 5 As shown, with noise standard deviations of 0.1, 0.2, 0.3, and 0.4 respectively, the loss function values ​​during training of the optimized dual-layer filter array and the unoptimized filter structure at different noise levels are compared. It can be seen that the loss function value of the dual-layer filter array is smaller and converges faster. Table 1 shows the comparison results of the mean square error of the images on the test set. As can be seen from Table 1, the reconstruction error of the optimized dual-layer filter array using the proposed method is significantly reduced.

[0050] Table 1 shows the comparison results of the mean square error of the images.

[0051] Noise Standard Deviation 0.1 0.2 0.3 0.4 Unoptimized filter structure 6.11E-05 6.38E-05 7.28E-05 9.19E-05 Filter array 5.32E-05 5.54E-05 6.89E-05 8.94E-05

[0052] This disclosure also proposes a spectral imaging system, wherein the target filter structure used in the system is obtained using the method described above. Specific implementations of the spectral imaging system provided in this disclosure can be found in the description of the method embodiments above; for brevity, they will not be repeated here.

[0053] This disclosure also proposes an application for the spectral imaging system obtained using the above method to perform spectral imaging or spatial spectral information acquisition. Specific implementations of the applications provided by this disclosure can be found in the description of the method embodiments above; for brevity, they will not be repeated here.

[0054] This disclosure also proposes a design apparatus for a spectral imaging system, comprising: an acquisition module, configured to acquire first response functions of multiple different first selectable filters and second response functions of multiple different second selectable filters, wherein each first response function represents the relative transmittance of the first selectable filter at different light wavelengths, and each second response function represents the relative transmittance of the second selectable filter at different light wavelengths; and a calculation module, configured to perform calculations using each first response function and each second response function to obtain the total response function of each dual-layer selectable filter structure, wherein each dual-layer selectable filter structure includes a... A first optional filter and a second optional filter, wherein at least some of the first optional filter and / or second optional filter of the dual-layer optional filter structure are different, and each total response function represents the relative transmittance corresponding to the dual-layer optional filter structure at different light wavelengths; a selection module is used to perform permutation orthogonal triangular decomposition on all total response functions to obtain a first result in which all total response functions are sorted according to the degree of correlation, and select multiple target response functions from the first result according to a preset selection rule, so as to form the target filter structure used by the spectral imaging system based on the dual-layer optional filter structure corresponding to each target response function.

[0055] In one possible implementation, each of the first optional filters is a quantum dot filter; and / or, the plurality of different second optional filters include a red filter, a green filter, and a blue filter.

[0056] In one possible implementation, the device further includes a decomposition module, configured to: perform permutational orthogonal triangular decomposition on the candidate response functions of all candidate filters in a preset database to obtain a second result in which all candidate response functions are sorted according to the strength of their correlation, wherein each candidate response function represents the relative transmittance of the candidate filter corresponding to a different light wavelength; if the second result is sorted from strong to weak correlation, the candidate filter corresponding to the first number of candidate response functions ranked last in the second result is selected as the first selectable filter; if the second result is sorted from weak to strong correlation, the candidate filter corresponding to the first number of candidate response functions ranked first in the second result is selected as the first selectable filter.

[0057] In one possible implementation, each of the total response functions is obtained as the product of the first response function of the first optional filter and the second response function of the second optional filter in the corresponding dual-layer optional filter structure.

[0058] In one possible implementation, multiple target response functions are selected from the first results according to a preset selection rule, including: if the first results are sorted from strong to weak correlation, the second number of total response functions ranked last in the first results are selected as the target response functions; if the first results are sorted from weak to strong correlation, the second number of total response functions ranked first in the first results are selected as the target response functions.

[0059] In one possible implementation, the device further includes a removal module for removing target response functions whose relative transmittance peak value is less than a preset threshold from the plurality of target response functions.

[0060] In one possible implementation, the device further includes an arrangement module for: arranging the relative positions of the dual-layer selectable filter structures corresponding to each target response function according to a preset arrangement method to obtain the target filter structure.

[0061] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0062] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium can be volatile or non-volatile.

[0063] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0064] This disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0065] Figure 6 A block diagram of a design apparatus for a spectral imaging system according to an embodiment of the present disclosure is shown. For example, apparatus 1900 may be provided as a server or terminal device. (Refer to...) Figure 6 The apparatus 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0066] Device 1900 may also include a power supply component 1926 configured to perform power management of device 1900, a wired or wireless network interface 1950 configured to connect device 1900 to a network, and an input / output interface 1958 (I / O interface). Device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM macOS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0067] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of the device 1900 to perform the above-described method.

[0068] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0069] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0070] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0071] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0072] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0073] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0074] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0076] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A design method for a spectral imaging system, characterized in that, include: Obtain first response functions of multiple different first optional filters and second response functions of multiple different second optional filters. Each first response function is used to represent the relative transmittance of the first optional filter under different light wavelengths, and each second response function is used to represent the relative transmittance of the second optional filter under different light wavelengths. The total response function of each dual-layer selectable filter structure is obtained by calculation using each of the first response functions and each of the second response functions. Each dual-layer selectable filter structure includes a first selectable filter and a second selectable filter. At least some of the first selectable filters and / or second selectable filters of the dual-layer selectable filter structures are different. Each total response function represents the relative transmittance of the dual-layer selectable filter structure at different light wavelengths. All total response functions are subjected to permutation orthogonal triangular decomposition to obtain a first result in which all total response functions are sorted according to the degree of correlation. Then, according to a preset selection rule, multiple target response functions are selected from the first result to form the target filter structure used by the spectral imaging system based on the dual-layer selectable filter structure corresponding to each target response function.

2. The method according to claim 1, characterized in that, Each of the first optional filters is a quantum dot filter; and / or, the plurality of different second optional filters include a red filter, a green filter, and a blue filter.

3. The method according to claim 2, characterized in that, The method further includes: The candidate response functions of all candidate filters in the preset database are subjected to permutation orthogonal triangular decomposition to obtain a second result in which all candidate response functions are sorted according to the degree of correlation. Each candidate response function represents the relative transmittance of the candidate filter at different light wavelengths. If the second result is sorted in order of correlation from strong to weak, the candidate filter corresponding to the first number of candidate response functions in the second result is taken as the first optional filter. If the second result is sorted from weakest to strongest correlation, the candidate filter corresponding to the first number of candidate response functions ranked first in the second result is taken as the first selectable filter.

4. The method according to any one of claims 1 to 3, characterized in that, The total response function is obtained by multiplying the first response function of the first optional filter and the second response function of the second optional filter in the corresponding dual-layer optional filter structure.

5. The method according to claim 1, characterized in that, According to preset selection rules, multiple target response functions are selected from the first result, including: If the first results are sorted in order of correlation from strong to weak, the second number of total response functions in the first results are taken as the target response function. If the first results are sorted in order of relevance from weak to strong, the total response function of the second number of results ranked first in the first results is taken as the target response function.

6. The method according to claim 1 or 5, characterized in that, The method further includes: Remove the target response functions whose relative transmittance peak value is less than a preset threshold from the plurality of target response functions.

7. The method according to any one of claims 1 to 3, characterized in that, The method further includes: According to a preset arrangement, the relative positions of the dual-layer selectable filter structures corresponding to each target response function are arranged to obtain the target filter structure.

8. A spectral imaging system, characterized in that, The target filter structure required by the system is obtained using the method described in any one of claims 1 to 7.

9. A design device for a spectral imaging system, characterized in that, include: The acquisition module is used to acquire a plurality of different first optional filters and a plurality of different second optional filters, wherein each first response function is used to represent the relative transmittance of the first optional filter under different light wavelengths, and each second response function is used to represent the relative transmittance of the second optional filter under different light wavelengths. The calculation module is used to calculate the total response function of each dual-layer selectable filter structure by using each of the first response functions and each of the second response functions. Each dual-layer selectable filter structure includes a first selectable filter and a second selectable filter. At least some of the first selectable filters and / or second selectable filters of the dual-layer selectable filter structures are different. Each total response function represents the relative transmittance of the dual-layer selectable filter structure at different light wavelengths. The selection module is used to perform permutation orthogonal triangular decomposition on all total response functions to obtain a first result in which all total response functions are sorted according to the degree of correlation. Then, according to a preset selection rule, multiple target response functions are selected from the first result to form the target filter structure used by the spectral imaging system based on the dual-layer selectable filter structure corresponding to each target response function.

10. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1 to 7 when executing instructions stored in the memory.

11. A spectral imaging system obtained by the method of any one of claims 1-7 for use in spectral imaging or spatial spectral information acquisition.

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