1. A super-resolution reconstruction method of a hyperspectral image is characterized by comprising the following steps:

acquiring a low-resolution hyperspectral image, a first high-resolution RGB color image and a second high-resolution RGB color image aiming at the same target object, wherein the first high-resolution RGB color image is acquired under a first light source, and the second high-resolution RGB color image is acquired under a second light source;

performing neighborhood matching on a low-resolution RGB color image obtained by converting the low-resolution hyperspectral image under the condition of a first light source and a first high-resolution RGB color image, and determining corresponding similar spectrum information of each matching point on a target high-resolution hyperspectral image, wherein the target high-resolution hyperspectral image is obtained by performing super-resolution reconstruction on the low-resolution hyperspectral image;

converting the first high-resolution RGB color image and the second high-resolution RGB color image into XYZ space respectively to obtain a first high-resolution XYZ image and a second high-resolution XYZ image;

and determining the real spectrum information of each matching point in the target high-resolution hyperspectral image according to the fitting result of the first high-resolution XYZ image, the second high-resolution XYZ image and the similar spectrum information.

2. The method of claim 1, further comprising:

and correcting two ends of the real spectrum information of each matching point according to the trend of the similar spectrum information of each pixel point in the first high-resolution RGB color image to obtain the corrected spectrum information of each matching point in the target high-resolution hyperspectral image.

3. The method according to claim 2, wherein the step of correcting both ends of the true spectral information of each matching point according to the trend of the similar spectral information of each pixel point in the first high-resolution RGB color image comprises:

and migrating the band trends at two ends of the similar spectrum information of each pixel point in the first high-resolution RGB color image to two ends of the real spectrum information of each matching point.

4. The method according to claim 1, wherein the step of matching the low-resolution RGB color image obtained by converting the low-resolution hyperspectral image with the first high-resolution RGB color image and determining corresponding similar spectral information of each matching point on the target high-resolution hyperspectral image comprises:

converting the low-resolution hyperspectral image into an RGB space under a first light source to obtain a low-resolution RGB color image;

matching each pixel point on a first high-resolution RGB color image collected under the first light source with a corresponding feature point on a corresponding neighborhood of the low-resolution RGB color image;

determining corresponding position points of the low-resolution hyperspectral image according to the feature points which are successfully matched, and extracting spectral information of each corresponding position point;

and taking the spectrum information extracted from the corresponding position point of the low-resolution hyperspectral image as the similar spectrum information of the target high-resolution hyperspectral image.

5. The method according to claim 1, wherein the step of converting the first high-resolution RGB color image and the second high-resolution RGB color image into XYZ space, respectively, resulting in a first high-resolution XYZ image and a second high-resolution XYZ image, comprises:

converting the RGB value of each pixel point in the first high-resolution RGB color image into XYZ values to obtain a first high-resolution XYZ image;

and converting the RGB value of each pixel point in the second high-resolution RGB color image into XYZ values to obtain a second high-resolution XYZ image.

6. The method according to claim 1, wherein the step of determining the true spectral information of each matching point in the target high-resolution hyperspectral image according to the fitting result of the first high-resolution XYZ image, the second high-resolution XYZ image and the similar spectral information comprises:

performing a fifth order polynomial fitting based on the XYZ values of the first high resolution XYZ image, the XYZ values of the second high resolution XYZ image, and the similar spectral information;

determining a functional relationship between the similar spectral information and the true spectral information based on a fitting result;

and reconstructing the real spectrum information of the target high-resolution hyperspectral image according to the functional relation and the similar spectrum information.

7. The method according to claim 1 or 2, characterized in that the method further comprises:

and reconstructing to obtain the target high-resolution hyperspectral image based on the real spectral information or the corrected spectral information of each matching point in the target high-resolution hyperspectral image.

8. A super-resolution reconstruction device of a hyperspectral image is characterized by comprising:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a low-resolution hyperspectral image, a first high-resolution RGB color image and a second high-resolution RGB color image aiming at the same target object, the first high-resolution RGB color image is acquired under a first light source, and the second high-resolution RGB color image is acquired under a second light source;

the matching module is used for performing neighborhood matching on a low-resolution RGB color image obtained by converting the low-resolution hyperspectral image under the condition of a first light source and the first high-resolution RGB color image, and determining corresponding similar spectrum information of each matching point on a target high-resolution hyperspectral image, wherein the target high-resolution hyperspectral image is obtained by performing super-resolution reconstruction on the low-resolution hyperspectral image;

the conversion module is used for converting the first high-resolution RGB color image and the second high-resolution RGB color image into an XYZ space respectively to obtain a first high-resolution XYZ image and a second high-resolution XYZ image;

and the determining module is used for determining the real spectrum information of each matching point in the target high-resolution hyperspectral image according to the fitting result of the first high-resolution XYZ image, the second high-resolution XYZ image and the similar spectrum information.

9. An electronic device comprising a memory, a processor, and a program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium, characterized in that a computer program is stored in the readable storage medium, which computer program, when executed, implements the method of any of claims 1-7.

Background

The spectral imaging technology can simultaneously acquire two-dimensional spatial image information and one-dimensional spectral information, and is widely applied to the fields of remote sensing, biomedicine and the like. However, the existing spectrometer cannot acquire a spectral image with a higher spatial resolution, when spectral information is acquired, the spatial resolution and the spectral resolution are always in balance, and the high spatial resolution cannot be achieved while the high spectral resolution is considered. Therefore, spatial super-resolution of hyperspectral images is a popular research direction in order to obtain images with relatively high spatial resolution and spectral resolution.

The method for enhancing the spatial resolution of the hyperspectral image comprises the step of converting a three-dimensional spectral image into a two-dimensional matrix, and the method can damage the spatial structure information of the original hyperspectral image and meanwhile has high requirements and dependency on the application function of a camera.

Disclosure of Invention

The invention aims to provide a super-resolution reconstruction method and a super-resolution reconstruction device for a hyperspectral image, which match similar spectra in a neighborhood matching mode and reserve the spatial structure information of an original hyperspectral image; a new polynomial correction model is provided to correct the similar spectrum, so that a high-resolution hyperspectral image can be reconstructed quickly and reconstruction accuracy is high.

In a first aspect, an embodiment of the present invention provides a super-resolution reconstruction method for a hyperspectral image, including:

acquiring a low-resolution hyperspectral image, a first high-resolution RGB color image and a second high-resolution RGB color image aiming at the same target object, wherein the first high-resolution RGB color image is acquired under a first light source, and the second high-resolution RGB color image is acquired under a second light source;

performing neighborhood matching on a low-resolution RGB color image obtained by converting the low-resolution hyperspectral image under the condition of a first light source and a first high-resolution RGB color image, and determining corresponding similar spectrum information of each matching point on a target high-resolution hyperspectral image, wherein the target high-resolution hyperspectral image is obtained by performing super-resolution reconstruction on the low-resolution hyperspectral image;

converting the first high-resolution RGB color image and the second high-resolution RGB color image into XYZ space respectively to obtain a first high-resolution XYZ image and a second high-resolution XYZ image;

and determining the real spectrum information of each matching point in the target high-resolution hyperspectral image according to the fitting result of the first high-resolution XYZ image, the second high-resolution XYZ image and the similar spectrum information.

With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the method further includes:

and correcting two ends of the real spectrum information of each matching point according to the trend of the similar spectrum information of each pixel point in the first high-resolution RGB color image to obtain the corrected spectrum information of each matching point in the target high-resolution hyperspectral image.

With reference to the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the step of correcting two ends of the real spectrum information of each matching point according to a trend of similar spectrum information of each pixel point in the first high-resolution RGB color image includes:

and migrating the band trends at two ends of the similar spectrum information of each pixel point in the first high-resolution RGB color image to two ends of the real spectrum information of each matching point.

With reference to the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the step of matching the low-resolution RGB color image obtained by converting the low-resolution hyperspectral image with the first high-resolution RGB color image and determining corresponding similar spectral information of each matching point on the target high-resolution hyperspectral image includes:

converting the low-resolution hyperspectral image into an RGB space under a first light source to obtain a low-resolution RGB color image;

matching each pixel point on a first high-resolution RGB color image collected under the first light source with a corresponding feature point in a corresponding field of the low-resolution RGB color image;

determining corresponding position points of the low-resolution hyperspectral image according to the feature points which are successfully matched, and extracting spectral information of each corresponding position point;

and taking the spectrum information extracted from the corresponding position point of the low-resolution hyperspectral image as the similar spectrum information of the target high-resolution hyperspectral image.

With reference to the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the step of converting the first high-resolution RGB color image and the second high-resolution RGB color image into XYZ space to obtain a first high-resolution XYZ image and a second high-resolution XYZ image respectively includes:

converting the RGB value of each pixel point in the first high-resolution RGB color image into XYZ values to obtain a first high-resolution XYZ image;

and converting the RGB value of each pixel point in the second high-resolution RGB color image into XYZ values to obtain a second high-resolution XYZ image.

With reference to the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the step of determining, according to a fitting result of the first high-resolution XYZ image, the second high-resolution XYZ image, and the similar spectral information, real spectral information of each matching point in a target high-resolution hyperspectral image includes:

performing a fifth order polynomial fitting based on the XYZ values of the first high resolution XYZ image, the XYZ values of the second high resolution XYZ image, and the similar spectral information;

determining a functional relationship between the similar spectral information and the true spectral information based on a fitting result;

and reconstructing the real spectrum information of the target high-resolution hyperspectral image according to the functional relation and the similar spectrum information.

With reference to the first aspect, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect, where the method further includes:

and reconstructing to obtain the target high-resolution hyperspectral image based on the real spectral information or the corrected spectral information of each matching point in the target high-resolution hyperspectral image.

In a second aspect, an embodiment of the present invention further provides a super-resolution reconstruction apparatus for a hyperspectral image, including:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a low-resolution hyperspectral image, a first high-resolution RGB color image and a second high-resolution RGB color image aiming at the same target object, the first high-resolution RGB color image is acquired under a first light source, and the second high-resolution RGB color image is acquired under a second light source;

the matching module is used for performing neighborhood matching on a low-resolution RGB color image obtained by converting the low-resolution hyperspectral image under the condition of a first light source and the first high-resolution RGB color image, and determining corresponding similar spectrum information of each matching point on a target high-resolution hyperspectral image, wherein the target high-resolution hyperspectral image is obtained by performing super-resolution reconstruction on the low-resolution hyperspectral image;

the conversion module is used for converting the first high-resolution RGB color image and the second high-resolution RGB color image into an XYZ space respectively to obtain a first high-resolution XYZ image and a second high-resolution XYZ image;

and the determining module is used for determining the real spectrum information of each matching point in the target high-resolution hyperspectral image according to the fitting result of the first high-resolution XYZ image, the second high-resolution XYZ image and the similar spectrum information.

In a third aspect, an embodiment provides an electronic device, including a memory and a processor, where the memory stores a computer program operable on the processor, and the processor implements the steps of the method described in any one of the foregoing embodiments when executing the computer program.

In a fourth aspect, embodiments provide a machine-readable storage medium having stored thereon machine-executable instructions that, when invoked and executed by a processor, cause the processor to carry out the steps of the method of any preceding embodiment.

The embodiment of the invention provides a super-resolution reconstruction method and a super-resolution reconstruction device for a hyperspectral image, which convert a low-resolution hyperspectral image to be reconstructed into a low-resolution RGB color image, and matching the image with a first high-resolution RGB color image under a first light source, wherein each pixel point on the first high-resolution RGB color image under the first light source is matched with a corresponding position point, obtaining similar spectral information of corresponding position points on the target high-resolution hyperspectral image, performing XYZ space conversion on the first high-resolution RGB color image and the second high-resolution RGB color image, fitting according to the converted XYZ image and the similar spectrum information to obtain a relation function of the similar spectrum information and the real spectrum information, and then, real spectrum information is determined, the space structure information of the original hyperspectral image is reserved, the high-resolution hyperspectral image can be reconstructed quickly, and the reconstruction precision is high.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.

In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

FIG. 1 is a flowchart of a super-resolution reconstruction method for hyperspectral images according to an embodiment of the invention;

FIG. 2 is a diagram illustrating a comparison between a reconstructed spectrum and a real spectrum according to an embodiment of the present invention;

fig. 3 is a functional module schematic diagram of a super-resolution reconstruction apparatus for hyperspectral images according to an embodiment of the invention;

fig. 4 is a schematic diagram of a hardware architecture of an electronic device according to an embodiment of the present invention.

Detailed Description

To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The existing method is to extract the end member spectrum of the low-resolution spectral image by a characteristic extraction method to obtain a spectral basis vector; and decomposing the high-resolution image based on regularization to obtain a coefficient matrix. And finally realizing super-resolution of the low-resolution spectral image by combining the coefficient matrix and the spectral basis vector.

In practical application, however, in the process of converting a three-dimensional spectral image into a two-dimensional matrix, the spatial structure information of the original image is destroyed by matrix decomposition and synthesis; moreover, the accuracy of the matrix decomposition method has a great dependence on the acquisition of the corresponding function of the camera and the spectral sensitivity function.

Based on the above, the super-resolution reconstruction method and device for the hyperspectral image provided by the embodiment of the invention can match similar spectra in a neighborhood matching manner, retain the spatial structure information of the original hyperspectral image, can rapidly reconstruct the high-resolution hyperspectral image and have higher reconstruction accuracy.

In order to facilitate understanding of the embodiment, firstly, a super-resolution reconstruction method for hyperspectral images disclosed by the embodiment of the invention is introduced in detail, and the application provides a method for enhancing the spatial resolution of a low-resolution hyperspectral image by means of neighborhood matching, fitting and trend migration of one low-resolution hyperspectral image and two corresponding color images from different light sources.

Fig. 1 is a flowchart of a super-resolution reconstruction method for hyperspectral images according to an embodiment of the present invention.

Referring to fig. 1, the method includes the steps of:

step S102, a low-resolution hyperspectral image, a first high-resolution RGB color image and a second high-resolution RGB color image which aim at the same target object are obtained, wherein the first high-resolution RGB color image is collected under a first light source, the second high-resolution RGB color image is collected under a second light source, the first high-resolution RGB color image corresponds to first light source information, and the second high-resolution RGB color image corresponds to second light source information.

Step S104, performing neighborhood matching on a low-resolution RGB color image obtained by converting the low-resolution hyperspectral image under the condition of a first light source and the first high-resolution RGB color image, and determining corresponding similar spectrum information of each matching point on a target high-resolution hyperspectral image, wherein the target high-resolution hyperspectral image is obtained by performing super-resolution reconstruction on the low-resolution hyperspectral image;

step S106, respectively converting the first high-resolution RGB color image and the second high-resolution RGB color image into an XYZ space to obtain a first high-resolution XYZ image and a second high-resolution XYZ image;

and S108, determining the real spectrum information of each matching point in the target high-resolution hyperspectral image according to the fitting result of the first high-resolution XYZ image, the second high-resolution XYZ image and the similar spectrum information, wherein the matching point is a position point which is successfully matched.

In a preferred embodiment of practical application, a low-resolution hyperspectral image to be reconstructed is converted into a low-resolution RGB color image, the low-resolution RGB color image is matched with a first high-resolution RGB color image under a first light source, each pixel point on the first high-resolution RGB color image under the first light source is matched with a corresponding position point, similar spectrum information of the corresponding position point on a target high-resolution hyperspectral image is obtained, then, XYZ space conversion is performed on the first high-resolution RGB color image and a second high-resolution RGB color image, fitting is performed according to the converted XYZ image and the similar spectrum information, a relation function of the similar spectrum information and real spectrum information is obtained, the real spectrum information is further determined, space structure information of the original hyperspectral image is reserved, the high-resolution hyperspectral image can be rapidly reconstructed, and reconstruction accuracy is high.

In some embodiments, the inventors have found that the CIE color matching function is insensitive to short and long wavelengths, and the inventors obtained the final corrected spectrum by re-correcting both ends of the obtained real spectrum according to the trend of the similar spectrum. And performing the operation on each point on the first high-resolution RGB color image to finally obtain a reconstructed high-resolution hyperspectral image. Exemplarily, the method further comprises:

step 1.1), correcting two ends of the real spectrum information of each matching point according to the trend of the similar spectrum information of each pixel point in the first high-resolution RGB color image to obtain the corrected spectrum information of each matching point in the target high-resolution hyperspectral image.

Wherein, when the step 1.1) is implemented, the method comprises the following steps: and migrating the band trends at two ends of the similar spectral information of each pixel point in the first high-resolution RGB color image to two ends of the real spectral information of each matching point.

It should be noted that, the method and the device shift the band trends at two ends of the similar spectrum to the real spectrum to correct the phenomenon of large errors caused by insensitivity of the color matching function to the bands at two ends, and finally obtain the reconstructed high-resolution hyperspectral image.

In some embodiments, step S104 may be further implemented by the following steps, specifically including:

step 2.1), converting the low-resolution hyperspectral image into an RGB space under a first light source to obtain a low-resolution RGB color image;

step 2.2), matching each pixel point on the first high-resolution RGB color image collected under the first light source with a corresponding feature point on a corresponding neighborhood of the low-resolution RGB color image;

step 2.3), determining corresponding position points of the low-resolution hyperspectral image according to each successfully matched feature point, and extracting spectral information of each corresponding position point;

and 2.4) taking the spectrum information extracted from the corresponding position point of the low-resolution hyperspectral image as the similar spectrum information of the target high-resolution hyperspectral image.

And matching similar RGB values of the low-resolution RGB color image and each pixel point on the shot first high-resolution RGB image in a neighborhood matching mode, wherein the position of the matched characteristic point on the low-resolution RGB image corresponds to the position of the low-resolution hyperspectral image, and the spectral information of the low-resolution hyperspectral image is extracted from the position of the low-resolution hyperspectral image and is used as the similar spectral information of the high-resolution hyperspectral image at the position.

It can be understood that the pixel points on the first high-resolution RGB image, the feature points on the low-resolution RGB color image, the corresponding position points of the low-resolution hyperspectral image, and the corresponding position points of the high-resolution hyperspectral image correspond in sequence according to the above.

In some embodiments, step S106 further comprises the steps of:

and 3.1) converting the RGB value of each pixel point in the first high-resolution RGB color image into XYZ values to obtain a first high-resolution XYZ image.

And 3.2) converting the RGB value of each pixel point in the second high-resolution RGB color image into XYZ values to obtain a second high-resolution XYZ image.

In some embodiments, step S108 further comprises:

step 4.1), performing fifth-order polynomial fitting based on the XYZ values of the first high-resolution XYZ image, the XYZ values of the second high-resolution XYZ image and the matched similar spectrum;

step 4.2), determining a functional relation between the similar spectrum information and the real spectrum information based on the fitting result;

and 4.3) reconstructing real spectral information of the high-resolution and high-spectrum target through the functional relation and the similar spectral information.

The method comprises the steps of converting three primary colors of an RGB space into color coordinate XYZ values in an XYZ space through color management of pixel points on a high-resolution RGB color image, wherein an association function relationship exists between a real spectrum and a similar spectrum, so that the function relationship between the real spectrum and the similar spectrum is solved through a fitting mode through an XYZ calculation model, and the function relationship and the similar spectrum are used for reconstructing the real spectrum. The XYZ calculation model is a polynomial correction model, and fitting is performed by inputting XYZ values of the two images and corresponding similar spectral information matched with the two images.

In some embodiments, the method provided in embodiments of the present invention further comprises:

and 5.3) reconstructing to obtain the target high-resolution hyperspectral image based on the real spectral information or the corrected spectral information of each matching point in the target high-resolution hyperspectral image.

In some embodiments, the dimension of the high resolution RGB color image from the CAVE dataset named "Balloon" is 512 × 512 × 3, and the corresponding dimension of the low resolution hyperspectral image from 400nm to 700nm is 64 × 64 × 31, and the lighting conditions are as follows: a light source D65 and a light source A; a 10 deg. field of view.

In the process of reconstructing the super-resolution of the hyperspectral imageThe method comprises the steps of firstly obtaining light source information, and obtaining a low-resolution hyperspectral image, an RGB color image under an A light source and an RGB color image under a D65 light source. And converting the low-resolution hyperspectral image into an RGB space under a D65 light source to obtain a low-resolution RGB color image. For each pixel point P on the high-resolution RGB color image under D65_D65i(i is the pixel location) of the pixel point P1 in the RGB color map with high resolution, the pixel location is (142, 444), the RGB value is [ 0.3850.3970.659 ]]^TFinding the characteristic point p1(36, 111), RGB value [ 0.2510.2510.496 ], whose position corresponds to the low-resolution RGB color image]^T。

As an optional embodiment, matching the RGB value of each pixel point with the found RGB value corresponding to the corresponding feature point in the preset domain range and performing similar RGB value, and determining the similar RGB value of the feature point. For example, the predetermined domain range is 8 × 8.

Illustratively, all RGB values within the 8 × 8 range of the feature point p1 point neighborhood are taken to be matched with points on the high-resolution RGB image as follows:

wherein, Δ R, Δ G, Δ B are the difference between the RGB value of each pixel point on the high-resolution RGB color image and the RGB value on the low-resolution neighborhood range of the feature point corresponding to the pixel point. Point p giving the smallest Δ E₁′[0.394 0.402 0.697]^TAnd as a similar RGB value, finding out a point corresponding to the characteristic point on the low-resolution hyperspectral image, and extracting the spectrum information as a similar spectrum r'.

Pixel point P of two high-resolution RGB color images under different light sources_D65[0.385 0.397 0.659]^TAnd P_A[0.311 0.150 0.113]^TConversion to XYZ-value T by color management_D65[41.968 41.316 69.169]^TAnd T_A[45.223 39.946 21.973]^TAnd then a fifth order polynomial fit is performed. Assuming that the true spectrum r and the similar spectrum r' exist in terms of a plurality of terms at the wavelength λThe formula is as follows:

wherein, a_iIs the polynomial coefficient and i is the order. The one-dimensional vector XYZ values can be considered as a dimensionality reduction of the one-dimensional vector spectral data, and the following computational model exists between the XYZ values x, y, z and the spectrum r:

wherein, the matrixColor matching function for CIE 10 ° field of view.

Therefore, replacing r in equation (3) by equation (2) yields the following relationship:

wherein the content of the first and second substances,to comprise a_iThe coefficient matrix of (2). The A weight matrix can be solved by inversion operation:

[-289.51 2710.13-10040.0 18464.9-16861.8 6118.21]^Tthe spectral reconstruction is performed by substituting the weight into equation (2). On the basis, the trends at two ends of the reconstructed spectrum are corrected again to solve the error caused by insensitivity of the color matching function to wave bands at two ends, the trends at two ends of the similar spectrum r' are transferred to the reconstructed spectrum, derivatives of similar spectrum curves at a 410nm end point and a 670nm end point are calculated firstly, and the formula is as follows:

the derivatives are then transferred to 410nm and 670nm of the reconstructed spectrum, completing the trend transfer, and finally obtaining the fully reconstructed spectrum.

For all pixel points P on the high-resolution RGB color image_iAnd performing the operations to finally obtain the reconstructed high-resolution hyperspectral image.

In some optional embodiments, the reconstructed target hyperspectral image can be evaluated by three evaluation indexes, namely peak signal to noise ratio (PSNR), Spectral Angle Mapping (SAM) and relatively dimensionless global Error (ERGAS). Wherein, table 1 shows the PSNR, ERGAS, SAM evaluation results, and the corrected spectrum of the spot position compared with the actual spectrum, as shown in fig. 2.

TABLE 1 super-resolution hyperspectral image evaluation results

	PSNR	ERGAS	SAM
				Balloon	51.426	0.373	1.552

In some embodiments, as shown in fig. 3, an embodiment of the present invention further provides a super-resolution reconstruction apparatus for hyperspectral images, including:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a low-resolution hyperspectral image, a first high-resolution RGB color image and a second high-resolution RGB color image aiming at the same target object, the first high-resolution RGB color image is acquired under a first light source, and the second high-resolution RGB color image is acquired under a second light source;

the matching module is used for performing neighborhood matching on a low-resolution RGB color image obtained by converting the low-resolution hyperspectral image under the condition of a first light source and the first high-resolution RGB color image, and determining corresponding similar spectrum information of each matching point on a target high-resolution hyperspectral image, wherein the target high-resolution hyperspectral image is obtained by performing super-resolution reconstruction on the low-resolution hyperspectral image;

the conversion module is used for converting the first high-resolution RGB color image and the second high-resolution RGB color image into an XYZ space respectively to obtain a first high-resolution XYZ image and a second high-resolution XYZ image;

and the determining module is used for determining the real spectrum information of each matching point in the target high-resolution hyperspectral image according to the fitting result of the first high-resolution XYZ image, the second high-resolution XYZ image and the similar spectrum.

In this embodiment, the electronic device may be, but is not limited to, a Computer device with analysis and processing capabilities, such as a Personal Computer (PC), a notebook Computer, a monitoring device, and a server.

As an exemplary embodiment, referring to fig. 4, the electronic device 110 includes a communication interface 111, a processor 112, a memory 113, and a bus 114, wherein the processor 112, the communication interface 111, and the memory 113 are connected by the bus 114; the memory 113 is used for storing a computer program for supporting the processor 112 to execute the image sharpening method, and the processor 112 is configured to execute the program stored in the memory 113.

A machine-readable storage medium as referred to herein may be any electronic, magnetic, optical, or other physical storage device that can contain or store information such as executable instructions, data, and the like. For example, the machine-readable storage medium may be: a RAM (random Access Memory), a volatile Memory, a non-volatile Memory, a flash Memory, a storage drive (e.g., a hard drive), any type of storage disk (e.g., an optical disk, a dvd, etc.), or similar storage medium, or a combination thereof.

The non-volatile medium may be non-volatile memory, flash memory, a storage drive (e.g., a hard drive), any type of storage disk (e.g., an optical disk, dvd, etc.), or similar non-volatile storage medium, or a combination thereof.

It can be understood that, for the specific operation method of each functional module in this embodiment, reference may be made to the detailed description of the corresponding step in the foregoing method embodiment, and no repeated description is provided herein.

The computer-readable storage medium provided in the embodiments of the present invention stores a computer program, and when executed, the computer program code may implement the method described in any of the above embodiments, and for specific implementation, reference may be made to the method embodiment, which is not described herein again.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.