1003 results about "Endoscopic image" patented technology

The invention discloses a Transform-CNN medical image segmentation method and system based on multi-scale fusion semantic enhancement, and relates to the technical field of image segmentation, and the method comprises the steps: collecting medical image data, and constructing a medical image data set; constructing a medical image segmentation network model; training the medical image segmentation network model through the medical image data set; and inputting real-time acquired data into the trained medical image segmentation network model to obtain a medical image segmentation result. According to the invention, double encoders, LGDA modules and the like are adopted to capture multi-scale global features of kidney tumors and enhance representation, and the LGDA module is used for adapting to size and form changes of the kidney tumors; the MLCF module is used for supplementing information of the main encoder; the PSE module captures multi-scale global semantic information and integrates local context information, local features are fused through the SAM module, and kidney tumor positioning in an endoscope image is achieved.

The invention discloses an image lesion recognition system for assisting a doctor in the digestive system department in sampling inspection. The image lesion recognition system comprises an image acquisition module, a preprocessing module, an AI lesion recognition module, a feature analysis module, a sampling recommendation module, a data storage module, a real-time interaction module, a system control center, a disinfection monitoring module and a power consumption optimization module. The system integrates white light, narrow-band light and fluorescent endoscope images based on a multi-modal large model, accurately identifies focuses such as polyps and ulcers in real time and marks boundaries, quantitatively analyzes the forms, infiltration depths and texture features of the focuses in combination with super-pixel segmentation and three-dimensional reconstruction technologies, and significantly improves the diagnosis objectivity. According to the system, a sampling scheme matched with a clinical guide is dynamically generated by relying on a reinforcement learning strategy and biomechanical simulation, a focus three-dimensional model and a real-time endoscope picture space are subjected to registration through an augmented reality technology, an operation path is guided by means of tactile feedback, and the accuracy and efficiency of doctor operation are greatly optimized.

The invention relates to an endoscope image reflection restoration method based on detection network and filtering fusion, and belongs to the technical field of medical image computer processing. The method aims at improving detection and removal of specular reflection in an endoscope image, a variant of TransUnet is adopted in the detection stage, and U-Net and Transform architectures are combined; the method comprises Atrous spatial pyramid pooling (ASPP), information bottleneck (IB) and a convolutional block attention module (CBAM). The enhancement functions improve the identifiability of specular highlight and solve the problem of weak generalization in endoscope data concentration. In the removal stage, the invention designs an enhanced median filtering module for removing specular reflection. The module first applies Gaussian blur to the repaired region and then calculates color information from the non-reflective region. According to the method, mirror reflection is effectively eliminated from the endoscope image, clearer and more accurate visual information is provided for clinicians, and improvement of diagnosis precision is facilitated.

The invention relates to the field of artificial intelligence, in particular to an endoscopic image description report generation method and device and a medium. According to the method, a multi-modal endoscope model comprising a visual encoder, a large language model and a cross-modal alignment module is constructed, image analysis and text generation are combined, integrated processing from image recognition to text description is achieved on an endoscope image, a description report is finally generated, and therefore efficient conversion from the image to the text is achieved; the speed and the automation degree of endoscopic image analysis are improved, and the time consumption of a doctor on report writing is reduced. According to the multi-modal endoscope model, the complex medical knowledge and the image analysis result are effectively combined, so that diagnosis suggestions with clinical value can be more accurately generated, namely, a more accurate description report is generated. The multi-modal endoscope model is trained by using a multi-task data set, the capabilities of different tasks can be integrated, and the prediction of a multi-task result is realized on the same endoscope picture.

The invention discloses a surgical instrument segmentation method based on a double prior guidance network, and the method specifically comprises the steps: constructing a hybrid encoder composed of a visual basic model and a state space model, and carrying out the coding processing of an input surgical endoscope image; the visual basic model is an SAM2-Hira model; the state space model is a MambaVision model; an adapter layer composed of a standard adapter and a Mama enhancement adapter is arranged between the coding layer and the decoding layer, and coding features are processed; setting a multi-context guide decoder, and performing multi-scale feature recovery and mask segmentation; a hybrid encoder, a Mama enhancement adapter and a multi-context guidance decoder form a double-priori guidance network DPG-Net, and high-precision surgical instrument segmentation is realized by using the DPG-Net. Accurate visual support is provided for the surgical robot, and the accuracy and safety of surgical operation are greatly improved.

The invention discloses an endoscope multi-region semantic perception time domain fusion adaptive white balance algorithm. The algorithm comprises the following steps: acquiring an endoscope image; performing tissue region division on the endoscope image through a lightweight semantic segmentation network to obtain a plurality of region images; converting each regional image from an RGB space to a YCbCr color space, separating a brightness channel Y from chrominance channels Cb and Cr, and calculating a channel mean value of the chrominance channels Cb and Cr; calculating the average offset of the chrominance channels Cb and Cr according to the calculated channel average of the chrominance channels Cb and Cr; performing white point detection on each regional image to obtain an RGB three-channel mean value; combining the calculated mean offset of the chromaticity channels Cb and Cr, the RGB three-channel mean obtained by white point detection of each regional image and a statistical index to form a multi-dimensional regional color feature vector; and fusing historical frame features with the regional color feature vector to obtain a feature vector based on space-time fusion.

A non-transitory machine-readable media may store instructions that, when run by one or more processors, cause the one or more processors to generate stereo endoscopic image data of a medical field and define a medical field volume from the stereo endoscopic image data. The processors may also project a 3D representation of the medical field volume to an extended-reality display device in a remote volume. The 3D representation may include a stereoscopic image and a 3D scene generated from the stereo endoscopic image data. The processors may also generate visual guidance in the remote volume to augment the stereo endoscopic image, map the visual guidance from the remote volume to the medical field volume to generate an augmented image of the medical field volume, and project the augmented image of the medical field volume to a display device viewed by an operator of instruments in the medical field.

The invention relates to an intelligent endoscope image evaluation method for ulcerative colitis, and the method comprises the steps: synchronously collecting video streams and three-dimensional point cloud data of a colonic mucosa through an endoscope system equipped with a structured light depth sensor, and constructing a three-dimensional coordinate system based on a colon anatomy trend; based on the three-dimensional point cloud data, constructing a complex topological structure on the surface of the colonic mucosa, and calculating a continuous homology feature set of the lesion area; and based on the persistent homology feature set, constructing a multi-scale graph neural network model of ulcerative colitis and Crohn disease specificity. By implementing the scheme, the structural and topological modeling of the colon mucosa lesion area can be realized, and the morphological characteristic difference between ulcerative colitis and Crohn's disease can be effectively captured. The multi-scale graph neural network fuses macroanatomical segmentation and micropathology features, efficient distinguishing of continuous and jumping lesions is achieved, finally, an intelligent and visual endoscope evaluation report is generated, and the accuracy and real-time performance of auxiliary diagnosis in an operation are improved.

The invention discloses an endoscope cold light source dimming method, system and device and a storage medium, and relates to the technical field of endoscope light source adjustment, and the method comprises the steps: obtaining a current image brightness value; according to the current image brightness value and the target image brightness value, a PID control algorithm is adopted, and a dimming strategy of the cold light source of the endoscope is determined; the dimming strategies sequentially comprise an exposure time dimming strategy, an LED lamp driving current dimming strategy and an electronic gain dimming strategy according to a priority sequence during dimming; and dimming the cold light source of the endoscope according to the dimming strategy. According to the method and the device, the cold light source of the endoscope can be quickly and stably dimmed, and the quality of an endoscope image is ensured.

The invention relates to the technical field of image analysis, in particular to an alimentary canal lesion analysis method and system based on image recognition, based on an endoscope image of an alimentary canal of a patient, the edge intensity of pixel points in the image is evaluated through image analysis, and a suspected lesion area in the image is recognized by adjusting an edge detection threshold value; the non-suspected lesion area and the suspected lesion area are distinguished, and a digestive tract area distinguishing result is obtained. According to the method, the identification process of the suspected lesion area and the non-suspected lesion area is optimized by dynamically adjusting the edge detection threshold value, the shape, size and color of the suspected lesion area are evaluated, and the suspected lesion area is matched with the known lesion type in the database, so that the lesion type identification accuracy is improved; and the image data corresponding to the lesion type is extracted, data support is provided for subsequent medical personnel to confirm the lesion type, and the evaluation of the healthy tissue is refined by analyzing the deviation between the non-suspected lesion area and the normal color data.

The invention discloses a defogging method based on an endoscope image and an endoscope system.The method is applied to a processor of the endoscope system.The method comprises the steps that the endoscope image is obtained; extracting a texture feature, at least one color feature and a global contrast feature of the endoscope image; determining that fog exists in the endoscope image according to the texture feature, the at least one color feature and the global contrast feature; determining the fog concentration of the endoscope image; and according to the fog concentration, carrying out defogging processing on the endoscope image so as to obtain a defogged image. The accuracy of fog detection and fog removal can be improved, automatic fog monitoring and removal are achieved, and the automation degree of the endoscope is improved.

The invention discloses a tumor image segmentation method and system of a multi-scale feature fusion network based on boundary enhancement, and relates to the technical field of image segmentation, and the method comprises the steps: obtaining a to-be-segmented tumor image; constructing a tumor image segmentation model based on a pyramid visual converter PVTv2 backbone network; training the tumor image segmentation model through the to-be-segmented tumor image and the known tumor image to obtain an optimal tumor image segmentation model; and acquiring a real-time to-be-segmented tumor image and inputting the real-time to-be-segmented tumor image into the optimal tumor image segmentation model to obtain a tumor image segmentation result. Aiming at the endoscope image segmentation of the kidney tumor, the kidney tumor in the endoscope image is efficiently, stably and automatically segmented through the boundary-enhanced multi-scale feature fusion network, clinical doctors are helped to provide accurate tumor area positioning in endoscopy and surgical operations, and compared with the most advanced method, the method has the advantages that the accuracy is high, and the efficiency is high. According to the method, better segmentation capability and stronger generalization capability are obtained.

The invention relates to the technical field of image focus detection, in particular to a fiber endoscope image focus detection method and system, and the method comprises the following steps: providing a data quality guide interface; capturing narrow-band light images of the endoscope away from a first preset position and a second preset position of the intestinal wall in the axial movement process, calculating brightness gain values of a blue light channel and a green light channel, calculating an asymmetric scattering correction coefficient, and updating indication of color information calibration integrity; tracking the pixel moving speed of the interferent in the view in the propulsion operation process, calculating the relative depth of the interferent, and updating the indication of the validity of the depth data of the interference layer; identifying the image area which is not shielded by the interferent, and updating the indication of the information coverage degree of the background area; triggering an image synthesis operation; performing color compensation on the image information of the image area which is stored in the background canvas and is not shielded by the interferent; reconstructing a clear image; and performing focus detection on the clear image. The method improves the accuracy of focus detection.

The invention discloses an endoscope image processing method and system for improving image quality, and relates to the technical field of image processing.The method comprises the steps that a highlight reflection area of an endoscope image is extracted by setting a detection and extraction mechanism of the highlight reflection area; the method comprises the following steps: dividing an image of which a highlight reflection region is extracted into a base layer and a detail layer, performing global illumination adjustment on the base layer, performing detail enhancement on the detail layer, and fusing the enhanced base layer and detail layer to obtain a pre-enhanced image; if the pre-enhanced image extracts an over-high-brightness area, repairing a high-brightness reflection area and outputting the high-brightness reflection area; according to the method, global enhancement of the endoscope image is realized through highlight reflection area detection, image hierarchical processing and highlight reflection area repair, and the visibility and availability of the endoscope image are improved.

The invention provides a real-time positioning and puncture track control method and system in a breast duct endoscope operation, and relates to the technical field of medical instruments, and the method comprises the following steps: establishing a real-time coordinate system by collecting an endoscopic image sequence and electromagnetic position data, obtaining lumen three-dimensional depth information by using a light field reconstruction algorithm, and adjusting and calculating stress field distribution by using a grid form. And determining an optimal track based on the puncture safety coefficient, and adjusting compensation parameters in real time by using the optical fiber strain sensing array. Accurate positioning and track control of the puncture instrument in the breast duct endoscope operation are achieved, and operation safety and accuracy are improved.

The invention belongs to the technical field of medical equipment. The endoscopic surgery operation quality real-time quantitative evaluation system comprises an endoscopic camera probe used for collecting endoscopic images and an endoscopic handle used for operation. The displacement sensor is integrated on the endoscope camera probe and is used for detecting the endoscope entering speed; the pressure sensor is integrated on the gas injection pipeline and is used for detecting the intracavity pressure; the force sensor is integrated on the endoscope handle and is used for detecting the tissue traction deformation; the optical coherence layer scanning probe is integrated on the endoscope camera probe and used for detecting the thickness of the submucous membrane, and the processing terminal is used for conducting real-time quantitative evaluation on the endoscopic surgery operation quality according to the endoscope entering speed, the intracavity pressure, the thickness of the submucous membrane, the tissue traction deformation image, the included angle between an instrument and a focus normal and the complication response time. According to the invention, full-process datamation real-time accurate evaluation of operation skills and operation safety of operators is realized.

The invention relates to the technical field of image processing, in particular to an endoscope image processing method based on photodynamic imaging and an endoscope imaging system.The endoscope image processing method based on photodynamic imaging comprises the steps that a focus image is collected through a photodynamic imaging endoscope, and preprocessing, three-dimensional reconstruction, combined reaction and curative effect database analysis are conducted; configuring an image processing module according to the request, and docking three-dimensional display; according to the method, the technical problem that the imaging request cannot be quickly and accurately responded due to the fact that the speed of endoscopic image processing through empirical data is low is solved, and the effects that the endoscopic image is finely processed, the photodynamic reaction features are extracted and the imaging request cannot be quickly and accurately responded are achieved. The technical effects that the optimal image enhancement parameter is adaptively searched, the imaging request is quickly responded, and the image targeting enhancement is performed are achieved, so that the focus area is accurately and visually displayed in a three-dimensional manner, and the output quality of the endoscope image is remarkably improved are achieved.

The invention relates to the technical field of medical image processing, in particular to a digestive endoscopy image deblurring enhancement method and system.The method comprises the steps that firstly, an input digestive endoscopy original image is processed through a blurred region classification network, and a pixel-level blurred classification map capable of distinguishing an adhesion blurred region and a motion blurred region is generated; then, parallel processing is carried out according to the classification graph: for an adhesion fuzzy region, physical model restoration and color correction are carried out by estimating a transmissivity graph and an ambient light value; for a motion blur region, a self-adaptive non-blind deconvolution kernel is constructed to perform deconvolution sharpness. And finally, inputting the two processing results and the original clear area into a multi-scale feature fusion network together, carrying out adaptive feature weighted fusion and image reconstruction, and outputting a globally clear and detail-enhanced final image. According to the method, accurate identification and targeted enhancement of composite blurring are realized, and the visual quality and diagnosis availability of the digestive endoscopy image are effectively improved.

The invention relates to the technical field of neurosurgical intelligent operations, and discloses a neuroendoscopic surgery intelligent puncture positioning system based on image navigation, and the system comprises a preoperative modeling module which constructs a preoperative tissue model and a corresponding elastic tensor field based on a three-dimensional medical image of a patient; the image fusion module is used for receiving the data of the preoperative modeling module, collecting intraoperative endoscopic images, predicting the tensor disturbance of tissues through a deep learning model, and feeding back the updated tensor disturbance to the tensor updating module; and the tensor updating module is in two-way communication with the image fusion module and is used for superposing tensor disturbance obtained by processing the intraoperative image with the preoperative tensor. Through fusion of an image navigation technology and preoperative modeling, a three-dimensional positioning frame based on neuroendoscopy operation requirements is constructed, intelligent planning and real-time correction of a puncture path are realized, important brain regions and vascular structures are effectively avoided, intra-operative risks are reduced, and positioning precision and safety are improved.

The invention relates to the technical field of medical image processing and intelligent diagnosis. The image recognition method for the urinary system calculus comprises the steps that a dynamic video stream of a calculus area is obtained through an endoscope image collection module, and a current frame image is extracted; performing illumination compensation processing on the image, eliminating vascular interference and light reflection, and enhancing the boundary of a calculus main body; based on multispectral feature fusion, separating yellow-like spectrum features of uric acid stones and grey-white-like spectrum features of calcium stones, and calculating stone surface color distribution dispersion; performing multi-angle texture energy detection by adopting a direction sensitive convolution kernel to generate a calcification crystallization density coefficient; inputting the color distribution dispersion and the calcification crystallization density coefficient into a decision tree model, and outputting a calculus type identifier; and according to the stone type identifier and the three-dimensional projection area of the stone contour, frequency-energy combination parameters of the laser stone crusher are generated, and the laser pulse emission time sequence is controlled in real time.

The invention relates to the technical field of image data processing, in particular to an endoscope image intelligent optimization method and system. The method comprises the following steps: acquiring a grayscale image of an endoscope image of a stomach, acquiring an edge image of the grayscale image, and performing region growth on pixel points with gradient values greater than a preset gradient in the edge image to obtain a plurality of first connected domains; according to the area of the minimum enclosing rectangle of the first connected domain and the number of edge pixel points, determining a suspected lesion area in the grayscale image, and taking the minimum grayscale value in the suspected lesion area as an initial segmentation threshold value; and gradually increasing the segmentation threshold value from the initial segmentation threshold value, segmenting the suspected lesion area to obtain a plurality of binary images, and determining a target lesion area according to the difference degree of newly added pixel points between the adjacent binary images in discreteness characteristics. By means of the technical scheme, the accurate focus area in the endoscope image of the stomach can be obtained.

A multispectral endoscope image intelligent fusion method relates to the technical field of medical image processing, and mainly comprises the following steps: directionally enhancing images in different channels; determining a target of image fusion network design; on the basis of a U-Net network architecture, a multi-expert encoder, a medical priori enhanced cross-channel attention mechanism, a focus adaptive spectral attention decoder and a physically driven reflectivity-illumination decoupling module are introduced, and a deep learning fusion network architecture with pathological interpretation is constructed; jointly optimizing image quality and medical priori knowledge to construct a loss function; a plurality of groups of four-channel endoscope images are collected, and staged progressive training and optimization are carried out. The method has multispectral feature fusion capability and physical interpretability, cross-scale fusion of deep blood vessels and mucous membrane textures can be achieved through a cross attention mechanism, and the visual identification degree and diagnosis specificity of early lesions are remarkably improved.

The invention provides a feature point matching method for digestive tract capsule endoscope images. The method comprises the following steps: after preprocessing, converting each image into a grayscale image, sequentially rotating each grayscale image from an initial angle according to different angles, carrying out feature point identification on the image after each rotation, and taking a subsequent feature point set with the maximum number of feature points as a feature point set; feature point matching is carried out on the feature point sets of the two adjacent frames of grayscale images, and part of matched feature points in the matched feature point sets are removed according to feature point position threshold conditions, feature point density threshold conditions and feature point brightness threshold conditions; and according to a geometric consistency condition and a confidence threshold condition, removing a part of matched feature points in the preliminary matched feature point set to obtain a final matched feature point set. According to the method, more feature points can be obtained, the distribution uniformity of the feature points is improved, and the quality of the feature points is improved.

The invention discloses a capsule endoscopic image super-resolution reconstruction method based on deep learning. The method comprises the steps of designing a data enhancement and label mechanism for pollution shielding; optimizing the ESRGAN model in combination with a lightweight structure and an attention mechanism; generating a structure sensing label; and training the model. The invention provides an image super-resolution reconstruction method fused with medical structure perception in order to solve the problems that a capsule endoscopic image is low in resolution, serious in pollution and shielding, difficult in structural detail recognition and the like in an actual clinical environment. According to the method, a pollution simulation data enhancement mechanism and a structural mask label system are constructed, so that the attention capability of the model on medical key information is effectively improved; a super-division network architecture combining lightweight improvement and attention mechanism optimization is adopted, so that the reconstruction quality is ensured, and meanwhile, the computing resource demand is greatly reduced; and a structure weighting loss function and an edge perception loss strategy are designed, so that the visual performance of the focus area is obviously enhanced.

The invention discloses a digital image enhancement method and system based on an endoscope, and aims to solve the problem of imaging limitation of a traditional endoscope in multi-focus positioning and targeted intervention. According to the digital image enhancement method based on the endoscope, endoscope image acquisition, wireless transmission, abnormal anchor point identification and controllable terminal targeted intervention operation can be carried out. The enhancement system for realizing the digital image enhancement method based on the endoscope identifies a suspected lesion point through image analysis, the control unit sends a release instruction to enable the endoscope to release the controllable terminal and establish communication, the scheduling system navigates the controllable terminal to an abnormal anchor point according to position information, and the abnormal anchor point is sent to the control unit. A focus image after digital image enhancement is collected and transmitted, and a judgment unit analyzes whether a target focus with an intervention value exists or not. According to the method, the digital image in the human body cavity can be enhanced, and the focus recognition accuracy and intervention efficiency are improved.

The image processing device 1X mainly includes a three-dimensional reconstruction means 31X, a matching means 32X, a complement means 33X, and a display control means 34X. The three-dimensional reconstruction means generates, based on endoscopic images of an examination target captured by a photographing unit provided in an endoscope, reconstruction data obtained by three-dimensionally reconstructing the examination target. The matching means performs matching between a three-dimensional model of the examination target and the reconstruction data. The complement means generates, based on a result of the matching, complemented reconstruction data which is the reconstruction data complemented with the three-dimensional model. The display control means displays the complemented reconstruction data on a display device.

The invention provides a real-time processing method for endoscopic image blood vessel enhancement and microcirculation evaluation, which comprises the following steps of: processing an original endoscopic image, and locally enhancing a brightness channel through a contrast-limited adaptive histogram equalization technology; self-adaptive frequency domain-space domain decomposition is realized based on local texture complexity analysis; performing multi-scale Hessian matrix blood vessel detection on the low-frequency component; applying a directional Gabor filter bank to the high-frequency component; spatial-temporal feature fusion is realized through multi-resolution pyramid optical flow calculation; synchronously completing blood vessel probability prediction, blood vessel diameter estimation and blood flow direction prediction by using a lightweight multi-task deep learning network; the blood flow velocity is analyzed and calculated based on a speckle mode, the perfusion density is subjected to accelerated statistics through an integrogram, and vascular morphological parameters are extracted by adopting an improved skeleton algorithm. According to the invention, an enhanced blood vessel visualization effect and a real-time microcirculation quantitative evaluation function can be provided, and the overall improvement of the endoscope image processing quality and efficiency is realized.

The invention relates to the field of endoscope image processing, and particularly discloses an automatic lesion recognition system for endoscopy of the digestive system department, which is characterized in that after a preprocessed original endoscope image is acquired, a double-branch parallel processing architecture is used to acquire characteristics with low resolution and rich semantic information through a deep context branch, and the characteristics of the original endoscope image are acquired. The potential area of the focus is accurately deduced; meanwhile, the fine texture of the mucous membrane is captured in a lossless manner through shallow detail branches which keep high resolution in the whole process. Furthermore, through a context-guided asymmetric enhancement mechanism, a global view of a deep branch is utilized to generate an uncertainty perception attention map as a reference, and weak detail features corresponding to a potential focus area in a shallow branch are accurately irradiated and adaptively enhanced. Thus, a conservative enhancement strategy is adopted in an uncertain focus area, background noise is effectively inhibited, and therefore the detection sensitivity and robustness of low-contrast and flat focuses are fundamentally improved.

The invention relates to a tracheal intubation navigation method and device based on image segmentation and three-dimensional reconstruction, and belongs to the field of wisdom medicines.The tracheal intubation navigation method comprises the steps that an endoscope image frame sequence in the intubation process is collected, and image segmentation is conducted on the endoscope image frame sequence to obtain an intubation head segmentation profile diagram; the method comprises the following steps: performing three-dimensional reconstruction on the interior of a trachea by using an endoscope image frame sequence and adopting three network branches of monocular scene three-dimensional reconstruction, inter-frame multi-view geometric pose estimation and optical flow matching three-dimensional reconstruction to obtain a three-dimensional point cloud model of the interior of the trachea; performing pose estimation on the head of the trachea cannula through the segmentation contour of the instrument at the head of the trachea cannula in the endoscope image to obtain a three-dimensional point cloud model of the head of the trachea cannula and instrument estimation pose sequence information; and inputting the trachea internal three-dimensional point cloud model, the intubation head three-dimensional point cloud model and instrument estimation pose sequence information into a Unreal Engine virtual engine to generate a digital twin dynamic navigation image in the intubation process.

The invention discloses an ultra-wide-spectrum endoscopic imaging device capable of being used for short-wave infrared fluorescence imaging, which is used for in-vivo minimally invasive imaging and comprises an illumination module, a wide-spectrum image transmission module, an imaging module and an image processing module. The illumination module consists of a light source and a light guide optical fiber, and is used for generating illumination light from ultraviolet to short wave infrared band and transmitting the illumination light to the wide-spectrum image transmission module; the wide-spectrum image transmission module consists of a few-interface optical imaging channel and a light guide illumination channel, and is used for illuminating an imaging target, exciting target fluorescence and collecting image signals from ultraviolet to short-wave infrared bands; the imaging module is composed of an optical filter and an imaging camera, and the imaging camera can be a wide spectral response image sensor or can be formed by combining cameras working in different wavebands and is used for converting optical images in different wavebands into image data; and the image processing module is used for registering and fusing image data of different wavebands in real time and displaying the image data.