CN114587272A - In-vivo fluorescence imaging deblurring method based on deep learning - Google Patents
In-vivo fluorescence imaging deblurring method based on deep learning Download PDFInfo
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Abstract
The invention belongs to the technical field of fluorescence imaging, and discloses a living body fluorescence imaging deblurring method based on deep learning, which utilizes the characteristic that a near-infrared two-zone fluorescence image has high resolution, and adopts a near-infrared one-zone fluorescence camera and a near-infrared two-zone fluorescence camera with a common visual field to simultaneously acquire the fluorescence image of a mouse; randomly dividing the collected living body fluorescence image into a training data set, a verification data set and a test data set according to a certain proportion; constructing a generation countermeasure network GAN for converting a near-infrared first-region fluorescence image into a near-infrared second-region fluorescence image by using a full gradient loss method, and training the acquired living body fluorescence image by using the constructed generation countermeasure network; and calculating the near-infrared first-zone fluorescence image by adopting the trained network to obtain the deblurred fluorescence image. The deblurred fluorescence image has the sensitivity of a near-infrared first-region fluorescence image and also has the definition similar to a near-infrared second-region fluorescence image.
Description
Technical Field
The invention belongs to the technical field of fluorescence imaging, and particularly relates to a living body fluorescence imaging deblurring method based on deep learning.
Background
At present, near-infrared fluorescence imaging mainly relies on a fluorescence probe labeling technology to track and observe change information of an interested area in biological tissues and cells, and has the advantages of rapidness, simplicity, convenience, no radiation damage, non-invasiveness, high sensitivity and the like.
The near-infrared fluorescence imaging can be divided into near-infrared first-zone fluorescence imaging with the wavelength of 700 nm-1000 nm and near-infrared second-zone fluorescence imaging with the wavelength of 1000 nm-1700 nm. The near-infrared first-zone imaging has the characteristics of low background noise and high signal intensity; but because of its light scattering effect is large, tissue autofluorescence and background interference are large, tissue penetration depth is small, imaging effect is fuzzy and resolution is low. The near-infrared two-region fluorescence imaging has the advantages of small scattering effect and absorption effect in biological tissues, deep penetration depth, small tissue autofluorescence and high signal-to-back ratio, clear effect and high resolution; however, because the intensity of the near-infrared two-zone fluorescence imaging signal is not high, some details of the imaging effect are not as good as those of the near-infrared one-zone fluorescence imaging. At present, the emission fluorescence spectrum of most fluorescent probes is in a near-infrared first region, and near-infrared second region fluorescence imaging cannot be carried out. Therefore, it is desirable to design a new deblurring method and system for in vivo fluorescence imaging to overcome the shortcomings of the prior art.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) because the light scattering effect of near-infrared first-region imaging is large, the tissue autofluorescence and background interference are large, the tissue penetration depth is small, the imaging effect is fuzzy and the resolution is low.
(2) Because the intensity of the near-infrared two-zone fluorescence imaging signal is not high, some details of the imaging effect are not as good as those of the near-infrared one-zone fluorescence imaging.
(3) At present, the emission fluorescence spectrum of most fluorescent probes is in a near-infrared first region, and near-infrared second region fluorescence imaging cannot be carried out.
The difficulty in solving the above problems and defects is:
1. the first and second fluorescent cameras simultaneously image in the same field of view.
2. And (4) acquisition of a data set.
The significance of solving the problems and the defects is as follows:
1. two-zone fluorescence images can be obtained without the use of two-zone probes.
2. The fluorescent image with the sensitivity of the near-infrared first-region fluorescent image and the definition of the near-infrared second-region fluorescent image can be obtained.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a living body fluorescence imaging deblurring method based on deep learning and based on deep learning.
The invention is realized in such a way that a living body fluorescence imaging deblurring method based on deep learning comprises the following steps: the characteristic that the near-infrared two-zone fluorescence image has high resolution is utilized, and a near-infrared one-zone fluorescence camera and a near-infrared two-zone fluorescence camera with a common visual field are adopted to simultaneously acquire the fluorescence image of the mouse; randomly dividing the collected living body fluorescence image into a training data set, a verification data set and a test data set according to a certain proportion; constructing a generation countermeasure network GAN for converting a near-infrared first-region fluorescence image into a near-infrared second-region fluorescence image by a full-gradient loss method through a deep learning technology, and training the collected living body fluorescence image by adopting the constructed generation countermeasure network; and calculating the near-infrared first-zone fluorescence image by adopting the trained network to obtain a deblurred fluorescence image, wherein the deblurred fluorescence image has the sensitivity of the near-infrared first-zone fluorescence image and the definition of the near-infrared second-zone fluorescence image.
Further, the living body fluorescence imaging deblurring method based on the deep learning comprises the following steps:
step one, building a same-visual field near-infrared first-zone imaging system and a same-visual field near-infrared second-zone imaging system;
collecting a living body fluorescence image;
step three, constructing a data set;
step four, constructing and generating a countermeasure network by using a full gradient loss method;
and step five, obtaining the trained network and the deblurred near-infrared first-zone fluorescence image.
Further, the building of the same-visual field near-infrared first-zone and second-zone imaging system in the first step comprises the following steps:
the near-infrared first-zone fluorescence camera is used for acquiring a near-infrared first-zone fluorescence image; the optical filter is used for filtering optical signals below a 850nm waveband; the near-infrared two-zone fluorescence camera is used for collecting near-infrared two-zone fluorescence images; the optical filter is used for filtering optical signals below a 1300nm wave band; the beam splitting cube is used for simultaneously transmitting the fluorescence image to the near-infrared first-region fluorescence camera and the near-infrared second-region fluorescence camera; a convex lens with a focal length of 100 mm; a convex lens with a focal length of 100 mm; the lens is used for increasing the imaging visual field range; the reflecting mirror is used for reflecting the image of the mouse to the lens through a certain angle; an excitation light source for emitting excitation light; the computer is used for collecting fluorescence images formed by the near-infrared first-zone fluorescence camera and the near-infrared second-zone fluorescence camera; a gas anaesthesia device for anaesthetizing mice and providing oxygen.
Further, the acquiring of the living body fluorescence image in the second step includes:
starting an exciting light emitting device to emit exciting light with a certain wavelength and intensity to irradiate the mouse, wherein the ICG emits fluorescence; imaging by using the built in-vivo fluorescence imaging system with the same visual field of the near-infrared first region and the near-infrared second region; collecting a near-infrared first-region fluorescence image and a near-infrared second-region fluorescence image in a mouse body, and collecting the number of the required fluorescence images according to different parts.
The constructing the data set in the third step comprises the following steps:
randomly dividing the collected near-infrared first-zone living body fluorescence image and near-infrared second-zone living body fluorescence image of the same imaging target into a training data set, a verification data set and a test data set according to a certain proportion.
Further, the step four of constructing the generation countermeasure network by the full gradient loss method includes:
training the data set by a generation countermeasure network improved by a full gradient loss method; the gradient of the near-infrared first-region living body fluorescence image and the gradient of the near-infrared second-region living body fluorescence image are compared by minimizing the gradient of adjacent pixels of each pixel in the image, and then the gradient between the original image and the generated image is compared, so that a loss function is established, and feedback is provided for network training.
In a generation countermeasure network constructed by a full gradient loss method, a pre-trained VGG and ResNet model is used for calculating perception loss, the network is trained through a loss function with fixed hyper-parameters, and then the network is trained through a loss function with set hyper-parameters; the set hyper-parameters are adjusted according to loss change and the performance of a verification data set, and the set hyper-parameters comprise generator loss and peak signal-to-noise ratio obtained when each epoch is finished in the training process; the network carries out down sampling on the near-infrared first-region image through step convolution, meanwhile, the main characteristics are extracted through parameters of learning step convolution kernels, the image is zoomed through a ResNet and sub-pixel convolution layer structure, and finally, the network performance of each epoch is evaluated through a verification data set.
Further, the obtaining of the trained network and the deblurred near-infrared first-zone fluorescence image in the fifth step includes:
and predicting the blurred living body fluorescence image by using the trained generation countermeasure network to generate a high-definition deblurred living body fluorescence image.
And deblurring the near-infrared first-region fluorescence image through a trained algorithm network to obtain a deblurred fluorescence image, wherein the deblurred fluorescence image has the sensitivity of the near-infrared first-region fluorescence image and the definition of the near-infrared second-region fluorescence image.
Another object of the present invention is to provide a living body fluorescence imaging deblurring system applying the living body fluorescence imaging deblurring method based on deep learning, the living body fluorescence imaging deblurring system comprising:
the imaging system building module is used for building a same-visual field near infrared first-zone imaging system and a same-visual field near infrared second-zone imaging system;
the living body fluorescence image acquisition module is used for acquiring a near-infrared first-zone living body fluorescence image and a near-infrared second-zone living body fluorescence image;
the data set construction module is used for randomly dividing the near-infrared first-zone living body fluorescence image and the near-infrared second-zone living body fluorescence image into a training data set, a verification data set and a test data set according to a certain proportion;
the generation countermeasure network construction module is used for constructing a generation countermeasure network by utilizing a full gradient loss method;
the generation confrontation network training module is used for training the constructed data set by adopting a full-gradient loss method to improve a generation confrontation network training method so as to obtain a trained network;
and the network deblurring module is used for deblurring the near-infrared first-zone fluorescent image through the trained algorithm network to obtain the deblurred near-infrared first-zone fluorescent image.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
the characteristic that the near-infrared two-zone fluorescence image has high resolution is utilized, and a near-infrared one-zone fluorescence camera and a near-infrared two-zone fluorescence camera with a common visual field are adopted to simultaneously acquire the fluorescence image of the mouse; randomly dividing the collected living body fluorescence image into a training data set, a verification data set and a test data set according to a certain proportion; constructing a generation countermeasure network GAN for converting a near-infrared first-region fluorescence image into a near-infrared second-region fluorescence image by a full-gradient loss method through a deep learning technology, and training the collected living body fluorescence image by adopting the constructed generation countermeasure network; and calculating the near-infrared first-zone fluorescence image by adopting the trained network to obtain a deblurred fluorescence image, wherein the deblurred fluorescence image has the sensitivity of the near-infrared first-zone fluorescence image and the definition of the near-infrared second-zone fluorescence image.
By combining all the technical schemes, the invention has the advantages and positive effects that: the living body fluorescence imaging deblurring method based on deep learning provided by the invention utilizes the characteristic that a near-infrared two-region fluorescence image has high resolution, constructs a generation countermeasure network for converting the near-infrared one-region fluorescence image into the near-infrared two-region fluorescence image through a deep learning technology, predicts the infrared one-region living body fluorescence imaging by utilizing the network to obtain the deblurring effect, and the deblurred fluorescence image has the sensitivity of the near-infrared one-region fluorescence image and also has the definition similar to the near-infrared two-region fluorescence image.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart of a method for deblurring in vivo fluorescence imaging according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a deblurring method for in-vivo fluorescence imaging according to an embodiment of the present invention.
FIG. 3 is a block diagram of a de-blurring system for fluorescence imaging in vivo according to an embodiment of the present invention;
in the figure: 1. an imaging system building module; 2. a living body fluorescence image acquisition module; 3. a data set construction module; 4. generating a confrontation network construction module; 5. generating a confrontation network training module; 6. and a network deblurring module.
FIG. 4 is a diagram of a co-field of view near infrared first and second zone imaging system provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a method, a system, a computer device and an intelligent terminal for deblurring in vivo fluorescence imaging, which are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method for deblurring in vivo fluorescence imaging provided by the embodiment of the present invention includes the following steps:
s101, building a same-view near-infrared first-zone imaging system and a same-view near-infrared second-zone imaging system;
s102, collecting a living body fluorescence image;
s103, constructing a data set;
s104, constructing and generating a countermeasure network by using a full gradient loss method;
and S105, obtaining the trained network and the deblurred near-infrared first-zone fluorescence image.
The principle diagram of the in-vivo fluorescence imaging deblurring method provided by the embodiment of the invention is shown in FIG. 2.
As shown in fig. 3, the living body fluorescence imaging deblurring system provided by the embodiment of the present invention includes:
the imaging system building module 1 is used for building a same-visual field near infrared first-zone imaging system and a same-visual field near infrared second-zone imaging system;
the living body fluorescence image acquisition module 2 is used for acquiring a near-infrared first-region living body fluorescence image and a near-infrared second-region living body fluorescence image;
the data set construction module 3 is used for randomly dividing the near-infrared first-region living body fluorescence image and the near-infrared second-region living body fluorescence image into a training data set, a verification data set and a test data set according to a certain proportion;
the generation countermeasure network construction module 4 is used for constructing a generation countermeasure network by using a full gradient loss method;
the generation confrontation network training module 5 is used for training the constructed data set by adopting a full-gradient loss method to improve a generation confrontation network training method so as to obtain a trained network;
and the network deblurring module 6 is used for deblurring the near-infrared first-zone fluorescent image through the trained algorithm network to obtain the deblurred near-infrared first-zone fluorescent image.
The technical solution of the present invention is further described with reference to the following specific examples.
Example 1
The invention provides a living body fluorescence imaging deblurring method based on deep learning, and a deblurred fluorescence image has the sensitivity of a near-infrared first-region fluorescence image and also has the definition similar to a near-infrared second-region fluorescence image.
The living body fluorescence imaging deblurring method based on deep learning provided by the invention comprises the following contents:
(1) the self-built in-vivo fluorescence imaging system with the same visual field for the first near-infrared region and the second near-infrared region is adopted for imaging, and the imaging region and the imaging quantity can be adjusted according to requirements; and then randomly dividing the collected near-infrared first-region living body fluorescence image and near-infrared second-region living body fluorescence image into a training data set, a verification data set and a test data set according to a certain proportion.
(2) In the algorithm network, the gradient of a near-infrared first-region living body fluorescence image and the gradient of a near-infrared second-region living body fluorescence image are compared, and then the gradient between an original image and a generated image is compared, so that a loss function is established, and feedback is provided for network training.
(3) The method comprises the steps that a generation countermeasure network constructed by a full gradient loss method uses pre-trained VGG and ResNet models to calculate perception loss, the network is trained through a loss function with fixed super parameters, the parameters are adjusted according to loss change and performance of a verification data set, the loss of a generator and a peak signal-to-noise ratio are obtained when each epoch is finished in the training process, and finally the performance of the network of each epoch is evaluated through the verification data set.
(4) And calculating the near-infrared first-zone fluorescence image by adopting the trained network to obtain a deblurred fluorescence image, wherein the deblurred fluorescence image has the sensitivity of the near-infrared first-zone fluorescence image and also has the definition of the near-infrared second-zone fluorescence image.
Example 2
The invention adopts a self-built co-visual field near-infrared first-zone and second-zone imaging system to acquire and process images of a mouse, and the method comprises the following steps:
step 1: constructing a same-visual-field near-infrared one-zone and two-zone imaging system (see FIG. 4)
1. The near-infrared first-zone fluorescence camera is used for collecting a near-infrared first-zone fluorescence image; 2. an 850nm optical filter for filtering optical signals below 850 nm; 3. the near-infrared two-zone fluorescence camera is used for collecting near-infrared two-zone fluorescence images; 4. a 1300nm filter for filtering optical signals below a 1300nm wave band; 5. the beam splitting cube simultaneously transmits the fluorescence images to the near-infrared first-region fluorescence camera and the near-infrared second-region fluorescence camera; 6. a convex lens with a focal length of 100 mm; 7. a convex lens with a focal length of 100 mm; 8. the lens is used for increasing the imaging visual field range; 9. the reflector reflects the image of the mouse to the lens through a certain angle; 10. an excitation light source that emits excitation light; 11. the computer is used for collecting fluorescence images formed by the near-infrared first-zone fluorescence camera and the near-infrared second-zone fluorescence camera; 12. a gas anaesthesia device, anaesthetising the mice and providing oxygen.
Step 2: collecting a live fluorescence image
Mice were anesthetized and provided with oxygen using a gas anesthesia apparatus. Then injecting a probe ICG (indocyanine green) into the body of the mouse through a tail vein, starting an exciting light emitting device to emit exciting light with certain wavelength and intensity to irradiate the mouse, and then emitting fluorescence by the ICG (indocyanine green). Then, a self-built living body fluorescence imaging system with the same visual field of the first near-infrared region and the second near-infrared region is used for imaging; the method is used for collecting the near-infrared first-zone fluorescence image and the near-infrared second-zone fluorescence image in the mouse body, and the required number of the fluorescence images can be collected according to different parts.
And step 3: building a data set
Randomly dividing the collected near-infrared first-region living body fluorescence image and near-infrared second-region living body fluorescence image into a training data set, a verification data set and a test data set according to a certain proportion.
And 4, step 4: full-gradient loss method for constructing and generating countermeasure network
Training the data set by generating a countermeasure network improved by a full gradient loss method; in the algorithm network, the gradient of a near-infrared first-region living body fluorescence image and the gradient of a near-infrared second-region living body fluorescence image are compared, and then the gradient between an original image and a generated image is compared, so that a loss function is established, and feedback is provided for network training.
In the network, the sensing loss is calculated by using a pre-trained VGG (variable G) and ResNet model, and the network is trained by setting a loss function of a super parameter, wherein the set super parameter can be adjusted according to loss change and the performance of a verification data set. The network performs downsampling on the near-infrared first-region image through step convolution to reduce the size of the image and accelerate calculation, meanwhile, main features are extracted through parameters of a learning step convolution kernel, and the ResNet and a sub-pixel convolution layer structure are adopted to zoom the image to improve the performance of a training network.
And 5: obtaining a trained network
The trained generation countermeasure network can predict the blurred living body fluorescence image to generate a high-definition deblurred living body fluorescence image.
Step 6: obtaining the deblurred near-infrared one-zone fluorescence image
And deblurring the near-infrared first-region fluorescence image through a trained algorithm network to obtain a deblurred fluorescence image, wherein the deblurred fluorescence image has the sensitivity of the near-infrared first-region fluorescence image and the definition of the near-infrared second-region fluorescence image.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A living body fluorescence imaging deblurring method based on deep learning is characterized in that the living body fluorescence imaging deblurring method based on deep learning utilizes the characteristic that a near-infrared two-zone fluorescence image has high resolution, and adopts a near-infrared one-zone fluorescence camera and a near-infrared two-zone fluorescence camera with a common visual field to simultaneously acquire the fluorescence image of a mouse; randomly dividing the collected living body fluorescence image into a training data set, a verification data set and a test data set according to a proportion; constructing a generation countermeasure network GAN for converting a near-infrared first-region fluorescence image into a near-infrared second-region fluorescence image by a full gradient loss method through deep learning, and training the collected living body fluorescence image by adopting the constructed generation countermeasure network; and calculating the near-infrared first-zone fluorescence image by adopting the trained network to obtain a deblurred fluorescence image, wherein the deblurred fluorescence image has the sensitivity of the near-infrared first-zone fluorescence image and the definition of the near-infrared second-zone fluorescence image.
2. The depth-learning-based in-vivo fluorescence imaging deblurring method of claim 1, wherein the depth-learning-based in-vivo fluorescence imaging deblurring method comprises the steps of:
step one, building a same-visual field near infrared first-zone imaging system and a same-visual field near infrared second-zone imaging system;
collecting a living body fluorescence image;
step three, constructing a data set;
step four, constructing and generating a countermeasure network by using a full gradient loss method;
and step five, obtaining the trained network and the deblurred near-infrared first-zone fluorescence image.
3. The in-vivo fluorescence imaging deblurring method based on deep learning of claim 2, wherein the building of the same-view near-infrared first-zone and second-zone imaging system in the first step comprises: the near-infrared first-zone fluorescence camera is used for acquiring a near-infrared first-zone fluorescence image; the optical filter is used for filtering optical signals below a 850nm waveband; the near-infrared two-zone fluorescence camera is used for collecting near-infrared two-zone fluorescence images; the optical filter is used for filtering optical signals below a 1300nm wave band; the beam splitting cube is used for simultaneously transmitting the fluorescence image to the near-infrared first-region fluorescence camera and the near-infrared second-region fluorescence camera; a convex lens with a focal length of 100 mm; a convex lens with a focal length of 100 mm; the lens is used for increasing the imaging visual field range; the reflecting mirror is used for reflecting the image of the mouse to the lens through a certain angle; an excitation light source for emitting excitation light; the computer is used for collecting fluorescence images formed by the near-infrared first-zone fluorescence camera and the near-infrared second-zone fluorescence camera; a gas anaesthesia device for anaesthetizing mice and providing oxygen.
4. The method for deblurring in vivo fluorescence imaging based on deep learning of claim 2, wherein the acquiring the in vivo fluorescence image in the second step comprises: starting an exciting light emitting device to emit exciting light with a certain wavelength and intensity to irradiate the mouse, wherein the ICG emits fluorescence; imaging by using the built in-vivo fluorescence imaging system with the same visual field of the near-infrared first region and the near-infrared second region; collecting a near-infrared first-region fluorescence image and a near-infrared second-region fluorescence image in a mouse body, and collecting the quantity of the required fluorescence images according to different parts;
the constructing the data set in the third step comprises the following steps: randomly dividing the collected near-infrared first-zone living body fluorescence image and near-infrared second-zone living body fluorescence image of the same imaging target into a training data set, a verification data set and a test data set according to a certain proportion.
5. The in-vivo fluorescence imaging deblurring method based on deep learning of claim 2, wherein the full gradient depletion method in the fourth step of constructing the generation countermeasure network comprises: training the data set by a generation countermeasure network improved by a full gradient loss method; the gradient of the near-infrared first-region living body fluorescence image and the gradient of the near-infrared second-region living body fluorescence image are compared by minimizing the gradient of adjacent pixels of each pixel in the image, and then the gradient between the original image and the generated image is compared, so that a loss function is established, and feedback is provided for network training;
in a generation countermeasure network constructed by a full gradient loss method, a pre-trained VGG and ResNet model is used for calculating perception loss, the network is trained through a loss function with fixed hyper-parameters, and then the network is trained through a loss function with set hyper-parameters; the set hyper-parameters are adjusted according to loss change and the performance of a verification data set, and the set hyper-parameters comprise generator loss and peak signal-to-noise ratio obtained when each epoch is finished in the training process; the network carries out down sampling on the near-infrared first-region image through step convolution, meanwhile, the main characteristics are extracted through parameters of learning step convolution kernels, the image is zoomed through a ResNet and sub-pixel convolution layer structure, and finally, the network performance of each epoch is evaluated through a verification data set.
6. The in-vivo fluorescence imaging deblurring method based on deep learning of claim 2, wherein the obtaining of the trained network and the deblurred near-infrared first-region fluorescence image in the fifth step comprises: predicting the blurred living body fluorescence image by using the trained generation countermeasure network to generate a high-definition deblurred living body fluorescence image;
and deblurring the near-infrared first-region fluorescence image through a trained algorithm network to obtain a deblurred fluorescence image, wherein the deblurred fluorescence image has the sensitivity of the near-infrared first-region fluorescence image and the definition of the near-infrared second-region fluorescence image.
7. A living body fluorescence imaging deblurring system for implementing the living body fluorescence imaging deblurring method based on the deep learning of any one of claims 1 to 6, wherein the living body fluorescence imaging deblurring system comprises:
the imaging system building module is used for building a same-visual field near infrared first-zone imaging system and a same-visual field near infrared second-zone imaging system;
the living body fluorescence image acquisition module is used for acquiring a near-infrared first-zone living body fluorescence image and a near-infrared second-zone living body fluorescence image;
the data set construction module is used for randomly dividing the near-infrared first-zone living body fluorescence image and the near-infrared second-zone living body fluorescence image into a training data set, a verification data set and a test data set according to a certain proportion;
the generation countermeasure network construction module is used for constructing a generation countermeasure network by utilizing a full gradient loss method;
the generation confrontation network training module is used for training the constructed data set by adopting a full-gradient loss method to improve a generation confrontation network training method so as to obtain a trained network;
and the network deblurring module is used for deblurring the near-infrared first-zone fluorescent image through the trained algorithm network to obtain the deblurred near-infrared first-zone fluorescent image.
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