Spatial-spectral domain combined compressed sensing method for infrared spectral image

Abstract

The invention discloses a spatial-spectral domain joint compressed sensing method for an infrared spectral image, and belongs to the field of high-resolution spectral imaging and compressed imaging. The implementation method comprises the following steps: generating a spatial-spectral domain joint compression imaging coding matrix, loading the matrix as a coding template onto a DMD (Digital Micromirror Device), carrying out spatial modulation on target light information, carrying out spectral modulation on a spectral image subjected to spatial modulation by using a diffraction grating, and carrying out spectral imaging on the spectral image subjected to spatial modulation. And carrying out space down-sampling and spectrum down-sampling on the modulated image by using a low-resolution infrared camera to obtain a low-resolution two-dimensional aliasing image. The limitation of the spatial resolution and the spectral resolution of an infrared camera is broken through through spatial domain compression imaging and spectral domain compression imaging, and an original high-space and high-spectral-resolution infrared image can be recovered from a collected low-space and low-spectral-resolution infrared image by using a reconstruction algorithm. According to the invention, the spatial and spectral correlation of the spectral image is utilized, data compression is carried out before data acquisition through a compression imaging method, and the data volume of acquisition, storage and transmission is significantly reduced.

Description

Technical Field

[0001] The present invention relates to a compressed sensing method for infrared spectral image reconstruction, and in particular to a space-spectral domain joint compressed sensing image reconstruction method for infrared spectral images, belonging to the field of high-resolution spectral imaging and compressed imaging. Background Art

[0002] Spectral imaging is well-known for its good information acquisition ability and good spectral resolution, and has developed rapidly in recent years. However, due to the limitation of the spatial spectral resolution of the image acquisition equipment, the current development of spectral imagers is difficult to meet people's pursuit of spectral imaging with higher spatial spectral resolution. In addition, the spectral information collected from the target scene can be regarded as a three-dimensional data cube. The transmission channel cannot bear such a huge amount of data, which can easily lead to problems such as slow data transmission and high packet loss rate. In addition, the high data acquisition volume puts great pressure on the back-end storage device. Infrared spectral imaging is an important branch of spectral imaging. However, due to the long wavelength of infrared light and the low energy of infrared radiation, the size of a single pixel of the infrared detector needs to be designed larger than that of the visible light detector. Under the condition of the same chip size, the number of pixels is less. In addition, due to the limitation of the bandwidth of the spectrometer, the spatial resolution and spectral resolution are always mutually restricted, and it is difficult to obtain images with both high spatial resolution and high spectral resolution at the same time. Therefore, how to simultaneously obtain infrared spectral images with high spatial and high spectral resolution has become a problem that needs to be solved urgently.

[0003] In recent years, compressed sensing technology has attracted extensive attention from researchers due to its outstanding performance in the field of signal processing. The biggest advantage of this theory is that it can eliminate most of the redundant information and only retain a small amount of measurement values, thereby reducing the pressure on the subsequent data calculation. In the field of two-dimensional imaging, compressed sensing technology is mainly applied to spatial domain compression imaging based on transform domain sparse or gradient sparse. Spatial domain compression imaging can expand the spatial resolution of the camera with the help of high-resolution digital micromirrors (DMD). For three-dimensional spectral images, Coded Aperture Snapshot Spectral Imaging (CASSI) is a typical example of applying the principle of compressed sensing to spectral reconstruction. The CASSI system maps the three-dimensional information of the target to a two-dimensional detector array through DMD and grating. The information obtained is spatial and spectral aliasing information, and the complete information is reconstructed through the reconstruction algorithm. Cai et al. proposed a deep unfolding network DAUHST. The team designed a Half-Shuffle Transformer that can capture local and global dependencies at the same time. This mechanism is very helpful in reducing the amount of calculation while maintaining the reconstruction effect.

[0004] Although the research on compressed sensing of spectral images is very popular, these studies are basically limited to the visible light band, and are only compressed sensing in the spatial domain or spectral domain. There are few studies on compressed sensing in the infrared band and in the spatial and spectral domains at the same time. Therefore, we proposed a joint compressed sensing method for infrared spectral images in the spatial and spectral domains. Based on the compressed sensing theory, the high spatial resolution of the DMD is used to compensate for the low spatial resolution of the infrared detector, and the high spectral resolution of the grating is used to compensate for the low spectral resolution of the infrared detector. The original hyperspectral image is reconstructed using the deep learning method, thereby breaking through the spatial resolution and spectral resolution limitations brought by the infrared spectral imaging equipment and obtaining infrared spectral images with high spatial resolution and high spectral resolution. Summary of the invention

[0005] In order to solve the problem that existing infrared spectral images with high spectral resolution and high spatial resolution are difficult to obtain simultaneously, the purpose of the present invention is to provide a spatial-spectral domain joint compressed sensing method and device for infrared spectral images. The method first generates a spatial-spectral domain joint compressed imaging coding matrix, and then loads this matrix as a coding template onto the DMD to spatially modulate the target light information, and then uses a diffraction grating to spectrally modulate the spatially modulated spectral image, and finally uses a low-resolution infrared camera to spatially downsample and spectrally downsample the modulated image to obtain a low-resolution two-dimensional aliased image. The collected low spatial and low spectral resolution infrared image can be restored to the original high spatial and high spectral resolution infrared image using a reconstruction algorithm.

[0006] The purpose of the present invention is achieved through the following technical solutions.

[0007] A spatial-spectral domain joint compressed sensing method for infrared spectral images comprises the following steps:

[0008] Step 1: The target scene is imaged onto the DMD through the imaging system, and the target scene is split into original spectral images X∈R with multiple bands. H×W×C , where represents a real number set, the superscript H×W×C represents the data dimension; H and W represent the length and width of the image; C represents the number of spectral bands;

[0009] Step 2: Generate a coding template and use the coding template to spatially modulate X in step 1 to obtain a spatially modulated image Y′;

[0010] Use a random binary matrix M∈R H×WThe array is used as a coding template; then the coding template is used to encode X in step 1, that is, the coding template is loaded onto the DMD and X is encoded. At this time, the spatial dimension information of X changes, that is, the spatial modulation of X is completed, and the spatial modulated light Y′∈R is obtained. H×W×C , the modulation result of the i-th band in Y′ is recorded as Y′(:,:,i), then Y′(:,:,i) is expressed as follows:

[0011] Y'(:,:,i)=M⊙X(:,:,i),i=1,...,C (1)

[0012] Among them, X(:,:,i) represents the original image corresponding to the i-th band in X; ⊙ represents matrix dot multiplication;

[0013] Step 3: Y′ in step 2 reaches the diffraction grating through the condenser; adjust the position of the diffraction grating so that the information light is incident on the diffraction grating surface. The diffraction grating modulates the spectral dimension of Y′, and Y″ obtains spectrally modulated light. The modulation result of Y″ at (u, v) in the i-th grating image-side focal plane coordinate system is recorded as Y″(u, v, i), and the value of Y′ at (x, y) in the i-th grating object-side plane coordinate system is recorded as Y′(x, y, i), then Y″(u, v, i) is expressed as follows:

[0014] Y″(u,v,i)=Y′(x,y+n(i-λ c ), i), i=1, ..., C (2)

[0015] Where n is the grating dispersion rate, λ c represents the central wavelength of the grating, n(i-λ c ) represents the distance over which the light of the i-th wavelength band is dispersed;

[0016] Step 4: The imaging system images the Y″ obtained in step 3 onto the camera detection array. Since the spatial resolution of the DMD is higher than that of the camera, it is necessary to focus the light reflected by multiple micro-mirrors onto one pixel of the camera to achieve spatial domain compression. Since the detector plane is a two-dimensional plane, Y″ will be compressed into a two-dimensional image on the detector to achieve spectral domain compression.

[0017] The resolution of the low-resolution image collected by the camera is Ha×(W+n(C-1))a. Spatial-spectral domain compression is to perform spatial-domain downsampling and spectral-domain downsampling on the detector surface to obtain a compressed image Y. The pixel value of Y in the pth row and qth column in the camera coordinate system is denoted as Y(p, q). Then Y(p, q) is expressed as follows:

[0018]

[0019] Where a represents the spatial compression ratio; r and s represent the row and column coordinates of the modulated image, respectively; Y″(r, s, i) represents the value of the i-th band of the spectral modulation result Y″ at (r, s); r and s are expressed as:

[0020] r=(p-1) / a+1:p / a (4)

[0021] s=(q-1) / a+1:q / a (5)

[0022] Among them, the value ranges of p and q are p=1, 2, ..., Ha, q=1, 2, ..., (W+n(C-1))a respectively; colon: indicates increasing from the previous number with a step size of 1 until the next number;

[0023] Step 5: Use the reconstruction algorithm to reconstruct the compressed image Y obtained in step 4 to obtain an infrared spectrum image with high spatial resolution and high spectral resolution.

[0024] The reconstruction algorithm described in step five adopts a deep learning method.

[0025] The present invention also discloses a space-spectrum domain joint compressed sensing device for infrared spectral images, which is used to implement the space-spectrum domain joint compressed sensing method for infrared spectral images. A space-spectrum domain joint compressed sensing device for infrared spectral images includes two groups of imaging lenses, DMD, relay lens, diffraction grating, infrared camera and computer; the target or scene reaches the DMD after passing through the imaging lens, and the DMD loads the coding template according to the preset, and performs spatial modulation on the light information from the target; the modulated light information passes through the diffraction grating, and the light of different wavelengths is dispersed along the dispersion direction, and the light information of the target is spectrally modulated; the modulated light signal is then imaged by the imaging lens onto the infrared camera, and because it only passes through one dispersion element, the camera receives a two-dimensional image that aliases the two dimensions of space and spectrum; the obtained image is uploaded to the computer, and an image with high spatial and high spectral resolution is reconstructed by solving the reconstruction algorithm.

[0026] The imaging lens is used to image the target onto the digital micromirror array (DMD) and to image the DMD and grating onto the detection plane of the detector.

[0027] The function of DMD is to store the designed coding template and load it into DMD to spatially modulate the image imaged into DMD;

[0028] The function of the relay lens is to transmit the light reflected by the DMD to the diffraction grating;

[0029] The function of the diffraction grating is to disperse the target optical signal along the dispersion direction according to the wavelength, and to perform spectral modulation on the target optical signal;

[0030] The role of the infrared camera is to receive the modulated image;

[0031] The role of the computer is to run the spatial-spectral domain compressed imaging recovery algorithm and use the measurement matrix to reconstruct the original high spatial and high spectral resolution image from the two-dimensional aliased low-resolution image taken by the camera.

[0032] Beneficial effects:

[0033] 1. The present invention discloses a spatial-spectral domain joint compressed sensing method and device for infrared spectral images, which breaks through the limitations of spatial resolution and spectral resolution of infrared cameras through spatial-domain compressed imaging and spectral-domain compressed imaging, so that high-resolution spectral images can be collected using a low-resolution infrared camera, DMD, diffraction grating, imaging optical path and control equipment.

[0034] 2. The present invention discloses a spatial-spectral domain joint compressed sensing method and device for infrared spectral images. The spatial and spectral correlations of spectral images are utilized to compress data before data collection by applying a compressed imaging method. This makes it possible to complete the recording of high-resolution spectral images by collecting only a small amount of data, significantly reducing the amount of data collected, stored, and transmitted, and improving the efficiency of spatial-spectral domain joint compressed sensing. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of the principle of joint compression imaging in the spatial-spectral domain;

[0036] Figure 2 is a device structure diagram of the system;

[0037] Figure 3 It is a schematic diagram of the image modulation and acquisition process of space-spectrum domain compression imaging;

[0038] Figure 4 is a set of original spectral images in the embodiment;

[0039] Figure 5 is a spectral image reconstructed in the embodiment;

[0040] Among them, 1-target or scene, 2-imaging lens, 3-digital micromirror array DMD, 4-relay lens, 5-diffraction grating, 6-imaging lens, 7-infrared camera, 8-computer. DETAILED DESCRIPTION

[0041] In order to better illustrate the purpose and advantages of the present invention, the invention is further described below with reference to the accompanying drawings and examples.

[0042] The spatial resolution is 512×512, the pixel depth is 8 bits, and the spectral resolution is 10 nm in the wavelength range of 610-700 nm. Figure 4 As shown in the figure, the coding template and the diffraction grating are used for modulation, and a low-resolution image with a resolution of 256×265 is obtained after downsampling. Finally, a restoration algorithm is used to restore a 10-band reconstructed image with a spatial resolution of 512×512 and a spectral resolution of 10nm from the low-resolution image of 256×265 (as shown in the figure). Figure 5 As shown in the figure, the spatial resolution of the recovered image is increased by 4 times (2×2) and the spectral resolution is increased by 10 times from the acquired low-resolution image.

[0043] The compression imaging steps in this embodiment are as follows Figure 1 As shown, the spatial spectrum modulation and image compression process are as follows Figure 3 shown.

[0044] This embodiment discloses a spatial-spectral domain joint compressed sensing method for infrared spectral images, and the specific implementation steps are as follows:

[0045] Step 1: The target scene is imaged onto the DMD through the imaging system, and the target scene is split into original spectral images X∈R with multiple bands. H×W×C , where represents a real number set, the superscript H×W×C represents the data dimension; H and W represent the length and width of the target space; C represents the number of spectral bands;

[0046] The target used in this embodiment is an original spectral image X containing 10 bands from 610nm to 700nm, with a spectral resolution of 10nm, a spatial resolution of 512×512, and a pixel depth of 8 bits. That is, in this example, H=W=512, C=10.

[0047] Step 2: Generate a coding template and use the coding template to spatially modulate X in step 1 to obtain a spatially modulated image Y′;

[0048] Use a random binary matrix M∈R H×W The array is used as a coding template, in this example, H = W = 512; then the coding template is used to encode X in step 1, that is, the coding template is loaded onto the DMD and X is encoded. At this time, the spatial dimension information of X changes, that is, the spatial modulation of X is completed, and the spatial modulated light Y'∈R is obtained. H×W×C , the modulation result of the i-th band in Y' is recorded as Y'(:,:,i), which is expressed as follows:

[0049] Y'(:,:,i)=M⊙X(:,:,i),i=1,...,C (6)

[0050] Among them, X(:,:,i) represents the original image corresponding to the i-th band in X; ⊙ represents matrix dot multiplication (i.e., multiplying the corresponding positions in the two matrices);

[0051] Step 3: Y' in step 2 reaches the diffraction grating through the condenser; the diffraction grating is a multi-slit diffraction screen with a periodic repeating structure, which has the advantages of high spectral resolution and linear dispersion. Adjust the position of the diffraction grating so that the information light is incident on the grating surface. The grating equation at this time is:

[0052] dsinθ=mλ (7)

[0053] Where d is the grating constant, θ is the diffraction angle, m is the diffraction order, and λ is the wavelength. From the grating equation, we can see that different wavelengths of light correspond to different diffraction angles. Therefore, the diffraction grating can be used to separate light of different wavelengths to achieve the dispersion effect (assuming that the dispersion is only performed along the y-axis direction). At this time, the target spectral dimension information also changes. Therefore, the diffraction grating can be regarded as a spectral dimension modulation of the target light signal.

[0054] Adjust the position of the diffraction grating so that the information light is incident on the diffraction grating surface. The diffraction grating modulates the spectral dimension of Y′, and Y″ obtains spectrally modulated light. The modulation result of Y″ at (u, v) in the i-th grating image-side focal plane coordinate system is recorded as Y″(u, v, i), and the value of Y′ at (x, y) in the i-th grating object-side plane coordinate system is recorded as Y′(x, y, i). The mathematical expression of Y″(u, v, i) is as follows:

[0055] Y″(u,v,i)=Y′(x,y+n(i-λ c ), i), i=1, ..., C (8)

[0056] Where n represents the grating dispersion (i.e. the distance between unit wavelengths on the imaging focal plane), λ c represents the central wavelength of the grating, n(i-λ c ) represents the distance over which the light of the i-th band is dispersed; in this example, n = 2, λ c =610nm.

[0057] Step 4: The imaging system images the Y″ obtained in step 3 onto the camera detection array. Since the spatial resolution of the DMD is higher than that of the camera, it is necessary to focus the light reflected by multiple micro-mirrors onto one pixel of the camera to achieve spatial domain compression. Since the detector plane is a two-dimensional plane, Y″ will be compressed into a two-dimensional image on the detector to achieve spectral domain compression.

[0058] The resolution of the low-resolution image collected by the camera is Ha×(W+n(C-1))a, which is 256×265 in this example. The spatial-spectral domain compression is to perform spatial-domain downsampling and spectral-domain downsampling on the detector surface of the modulated image with a resolution of 512×530×10, and obtain a compressed image Y with a resolution of 256×265. The value of Y at the camera coordinate system (p, q) is recorded as Y(p, q), which is expressed as follows:

[0059]

[0060] Wherein, a represents the spatial compression ratio, which is 0.5 in this example; r and s represent the row and column coordinates of the modulated image, respectively; Y″(r, s, i) represents the value of the i-th band of the spectral modulation result Y″ at (r, s); r and s are expressed as:

[0061] r=(p-1) / a+1:p / a (10)

[0062] s=(q-1) / a+1:q / a (11)

[0063] Among them, the value ranges of p and q are p=1, 2, ..., Ha, q=1, 2, ..., (W+n(C-1))a respectively; colon: indicates increasing from the previous number with a step size of 1 until the next number;

[0064] Step 5: Use a reconstruction algorithm to reconstruct the compressed image Y obtained in step 4 to obtain an infrared spectrum image with high spatial resolution and high spectral resolution.

[0065] The image restoration algorithm described in step 5 adopts a deep learning method, and the network model used is the DAUHST model; the image is reconstructed using the compressed image Y, the measurement matrix H and the trained network model

[0066] In this example, a high-resolution image of 10 bands can be restored. Therefore, this example uses the spatial spectrum domain compression imaging method to achieve a 4-fold (2×2) increase in the spatial resolution of the image and a 10-fold increase in the spectral resolution. The restoration effect is as follows: Figure 4 In order to quantitatively analyze the restoration effect, the peak signal-to-noise ratio (PSNR) of the restored image is calculated, which is defined as follows:

[0067]

[0068] Among them, MSE represents the mean square error, X(:,:,i) represents the original image corresponding to the i-th band in X, express The reconstructed image corresponding to the i-th band in , MAX represents the maximum pixel value of the image.

[0069] A device for implementing the above method, such as Figure 2 As shown, it includes two groups of imaging lenses 2 and 6, DMD3, relay lens 4, diffraction grating 5, infrared camera 7 and computer 8; the target or scene 1 reaches DMD3 after passing through the imaging lens 2, and DMD3 loads the coding template according to the preset, and spatially modulates the light information from the target; the modulated light information reaches the diffraction grating 5 through the relay lens 4, and the light of different wavelengths is dispersed along the dispersion direction, and the light information of the target is spectrally modulated; the modulated light signal is then imaged by the imaging lens to the infrared camera 7, and because it only passes through one dispersion element, the camera receives a two-dimensional image that aliases the two dimensions of space and spectrum; the obtained image is uploaded to the computer 8, and an image with high spatial and high spectral resolution is reconstructed through the trained DAUHST network model. The reconstruction result is shown in FIG. Figure 5 shown.

[0070] The imaging lenses 2 and 6 respectively image the target onto the digital micromirror array DMD and image the DMD and grating onto the detection plane of the detector;

[0071] The function of DMD3 is to store the designed coding template and load it on DMD, and to perform spatial modulation on the image imaged on DMD;

[0072] The function of the relay lens 4 is to transmit the light reflected by the DMD to the diffraction grating;

[0073] The function of the diffraction grating 5 is to disperse the target optical signal along the dispersion direction according to the wavelength, and to perform spectral modulation on the target optical signal;

[0074] The infrared camera 7 is used to receive the modulated image;

[0075] The function of computer 8 is to run the spatial spectrum domain compression imaging recovery algorithm and reconstruct the original high spatial and high spectral resolution image from the two-dimensional aliased low-resolution image taken by the camera using the measurement matrix.

[0076] The specific description above further illustrates the purpose, technical solutions and beneficial effects of the invention in detail. It should be understood that the above is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A spatial-spectral domain joint compressed sensing method for infrared spectral images, characterized by: The steps include: Step 1: The target scene is imaged onto the DMD through the imaging system, and the target scene is split into original spectral images X∈R with multiple bands. H×W×C , where R represents a real number set, the superscript H×W×C represents the data dimension; H and W represent the length and width of the image; C represents the number of spectral bands; Step 2: Generate a coding template and use the coding template to spatially modulate X in step 1 to obtain a spatially modulated image Yπ; Use a random binary matrix M∈R H×W As a coding template; then use the coding template to encode X, that is, load the coding template onto the DMD and encode X. At this time, the spatial dimension information of X changes, that is, the spatial modulation of X is completed, and the spatial modulated light Y'∈R is obtained. H×W×C , let the modulation result of the i-th band in Y′ be recorded as Y'(:,:,i), then Y'(:,:,i) is expressed as follows: Y'(:,:,i)=M⊙X(:,:,i),i=1,...,C (1) Among them, X(:,:,i) represents the original image corresponding to the i-th band in X; ⊙ represents matrix dot multiplication; Step 3: Y' in step 2 reaches the diffraction grating through the condenser; adjust the position of the diffraction grating so that the information light is incident on the diffraction grating surface. The diffraction grating modulates the spectral dimension of Y' to obtain spectrally modulated light Y", and the modulation result of Y" at (u, v) in the i-th grating image focal plane coordinate system is recorded as Y" (u, v, i), and the value of Y' at (x, y) in the i-th grating object plane coordinate system is recorded as Y' (x, y, i), then Y" (u, v, i) is expressed as follows: Y”(u,v,i)=Y'(x,y+n(i-λ c ),i),i=1,...,C (3) Where n is the grating dispersion rate, λ c represents the central wavelength of the grating, n(i-λ c ) represents the distance over which the light of the i-th wavelength band is dispersed; Step 4: The imaging system images the Y" obtained in step 3 onto the camera detection array. Since the spatial resolution of the DMD is higher than that of the camera, it is necessary to focus the light reflected by multiple micro-mirrors onto one pixel of the camera to achieve spatial compression. Since the detector plane is a two-dimensional plane, Y" will be compressed into a two-dimensional image on the detector to achieve spectral compression. The resolution of the low-resolution image collected by the camera is Ha×(W+n(C-1))a. Spatial-spectral domain compression is to perform spatial-domain downsampling and spectral-domain downsampling on the detector surface to obtain a compressed image Y. The pixel value of Y in the pth row and qth column in the camera coordinate system is recorded as Y(p,q). Y(p,q) is expressed as follows: Where a represents the spatial compression ratio; r and s represent the row and column coordinates of the modulated image Y” respectively; Y” (r, s, i) represents the value of the i-th band of the spectral modulation result Y” at (r, s); r and s are expressed as: r=(p-1) / a+1:p / a (5) s=(q-1) / a+1:q / a (6) Wherein, the value ranges of p and q are p=1, 2, ..., Ha, q=1, 2, ..., (W+n(C-1))a respectively; the colon : indicates incrementing from the previous number with a step length of 1 until the next number; Step 5: Use the reconstruction algorithm to reconstruct the compressed image Y obtained in step 4 to obtain an infrared spectrum image with high spatial resolution and high spectral resolution.

2. The spatial-spectral domain joint compressed sensing method for infrared spectral images according to claim 1, characterized in that: The reconstruction algorithm described in step five adopts a deep learning method.

3. The device for implementing the spatial-spectral domain joint compressed sensing method for infrared spectral images as claimed in claim 1 is characterized by: It includes two groups of imaging lenses, DMD, relay lens, diffraction grating, infrared camera and computer. After the target or scene passes through the imaging lens, it reaches the DMD, and the DMD loads the coding template according to the preset, and performs spatial modulation on the light information from the target. After the modulated optical information passes through the diffraction grating, the light of different wavelengths is dispersed along the dispersion direction, and the optical information of the target is spectrally modulated; The modulated light signal is then imaged onto the infrared camera by the imaging lens. Since it only passes through one dispersion element, the camera receives a two-dimensional image that is a mixture of spatial and spectral dimensions. The obtained image is uploaded to the computer and reconstructed into an image with high spatial and spectral resolution through the solution of the reconstruction algorithm.

4. The device according to claim 3, characterized in that: The imaging lens is used to image the target onto the digital micromirror array (DMD) and to image the DMD and grating onto the detection plane of the detector. The function of DMD is to store the designed coding template and load it on DMD, and to spatially modulate the image imaged on DMD; The function of the relay lens is to transmit the light reflected by the DMD to the diffraction grating; The function of the diffraction grating is to disperse the target optical signal along the dispersion direction according to the wavelength, and to perform spectral modulation on the target optical signal; The role of the infrared camera is to receive the modulated image; The role of the computer is to run the spatial-spectral domain compressed imaging recovery algorithm and use the measurement matrix to reconstruct the original high spatial and high spectral resolution image from the two-dimensional aliased low-resolution image taken by the camera.

Images