3422results about How to "Improve image quality" patented technology

The invention provides a semiconductor chip defect automatic detection method and system based on hyperspectral imaging, and relates to the technical field of chip detection, and the method comprises the steps: collecting a chip hyperspectral image, carrying out the image enhancement and illumination non-uniformity correction through an improved neuromorphic adaptive processing network and a dual-path diffusion model, and carrying out the detection of the hyperspectral image. Extracting multi-level features by using a dynamic mixed feature enhancement network based on neural architecture search, fusing the features through a cross-layer attention mechanism, generating an initial defect detection result by using a multi-branch expert decision-making module and an uncertainty dynamic integration strategy, constructing a space-time knowledge inference graph, and determining a defect detection result; and performing reliability evaluation and optimization on the initial defect detection result in combination with the form similarity, the spatial position relationship and the process parameter influence model to obtain a final defect detection result.

The invention discloses a product detection method and system based on machine vision, and relates to the technical field of machine vision, and the method comprises the steps: initializing an ambient light sensor, evaluating an initial illumination condition, and dynamically adjusting a light source parameter to verify and lock an optimal setting; selecting a preset wavelength range to configure a spectral imaging system to collect image data according to the characteristics of the building material, performing primary processing and image synthesis to obtain a comprehensive spectral image, and performing feature extraction to obtain a high-quality image data set; preprocessing an image data set on edge equipment, deploying a deep learning model to carry out preliminary defect detection, identifying and marking defect types, and extracting local and frequency domain features; and refining defect types by using a classification algorithm according to the preliminary detection result, integrating a final classification result and image data, and transmitting to a cloud to complete detection. According to the invention, high-precision and high-efficiency product defect detection is realized.

The invention provides an optical lens, which comprises six lenses which sequentially comprise a first lens with negative focal power, a second lens with negative focal power, a third lens with negative focal power, a fourth lens with negative focal power, a fifth lens with negative focal power and a sixth lens with negative focal power from an object side to an imaging surface, the object side surface of the second lens is a concave surface, and the image side surface of the second lens is a convex surface; the object side surface and the image side surface of the third lens are convex surfaces; the fourth lens has positive focal power, and the object side surface of the fourth lens is a convex surface; the fifth lens has negative focal power, and the image side surface of the fifth lens is a concave surface; and the sixth lens has positive focal power. According to the optical lens provided by the invention, the six lenses with specific focal power are adopted, and through specific surface shape matching and reasonable focal power distribution, the imaging quality of the optical lens can be improved, the aberration can be reduced, the imaging quality of the optical lens can be improved, and the lens has one or more advantages of large target surface, large aperture, high imaging quality and the like.

The invention relates to a power station screen cabinet equipment temperature early warning method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a dual-light image of power station screen cabinet equipment to be analyzed according to acquisition parameters corresponding to current acquisition; the dual-light image comprises an infrared thermal imaging image and a visible light image; performing preprocessing, feature extraction processing, spatial registration processing and information fusion processing on the dual-light image to generate a fused image; performing temperature field reconstruction processing and hot spot detection processing on the fused image to obtain temperature field distribution information and hot spot area information; and according to the temperature field distribution information and the hot spot area information, performing temperature analysis on the power station screen cabinet equipment to obtain temperature early warning information of the power station screen cabinet equipment. By adopting the method, the temperature early warning accuracy of the power station screen cabinet equipment can be improved.

The invention provides a medical image segmentation method, which comprises the following steps: acquiring PET indirect development original image data, and preprocessing the image, including denoising, contrast enhancement and gray level normalization, to obtain a preprocessed PET image data set; designing segmentation middleware, integrating a plurality of segmentation algorithms including threshold segmentation, region growth and level set methods, and selecting proper segmentation algorithms for different types of lesion regions according to feature vector analysis results; pET image data with labels are generated through the simulation technology, a training data set is constructed in combination with real case data, the sample size is expanded through the data enhancement technology, and a large-scale data set used for algorithm training is generated; and the segmentation result of the U-Net is refined by using the segmentation middleware, the lesion area edge is focused on, and the lesion boundary is accurately positioned through an edge detection algorithm to obtain a fine segmentation result.

The invention relates to the technical field of data processing, and discloses a method and a system for detecting a soil body around a pipeline based on a ground penetrating radar. The method comprises the following steps: performing generalized reflection coefficient modeling on soil layer distribution data to obtain reflection coefficient model data, and calculating the reflection coefficient model data through a Bayesian inversion framework to obtain soil dielectric characteristic data; performing feature parameter extraction on the dielectric feature data of the soil body to obtain feature vector data of the soil body, and performing anomaly identification on the feature vector data of the soil body to obtain data of an abnormal region of the soil body; and performing fuzzy hierarchy analysis on the soil body abnormal region data to obtain risk index data, and performing correlation analysis on the risk index data and the pipeline position feature data to obtain risk assessment data of the soil body around the pipeline. Based on the obtained quantification result of the dielectric parameters of the soil body, the accuracy of abnormal region identification is improved, and the accuracy of detection of the soil body around the pipeline by the ground penetrating radar is further improved.

The invention provides an optical lens, which sequentially comprises a first lens with negative focal power from an object side to an imaging surface along an optical axis, a second lens with negative focal power, a third lens with positive focal power and a fourth lens with negative focal power from the object side to the imaging surface, the object side surface of the second lens is a concave surface, and the image side surface of the second lens is a concave surface; the object side surface of the third lens is a convex surface, and the image side surface of the third lens is a concave surface; the object side surface of the fourth lens is a convex surface, and the image side surface of the fourth lens is a convex surface; the object side surface of the fifth lens is a convex surface, and the image side surface of the fifth lens is a convex surface; the object side surface of the sixth lens is a concave surface, and the image side surface of the sixth lens is a concave surface; the object side surface of the seventh lens is a convex surface, and the image side surface of the seventh lens is a convex surface; and the object side surface of the eighth lens is a convex surface, and the image side surface of the eighth lens is a concave surface. According to the optical lens, through specific surface shape matching and reasonable focal power distribution, the imaging quality of the optical lens can be improved, and the optical lens has the advantage of being good in imaging quality.

The invention relates to the field of image processing, in particular to a paint defect detection method and system based on machine vision, paint surface images are collected through an industrial camera, and brightness correction is carried out in combination with an illumination correction factor; fusing color fluctuation and texture features to construct a fluctuation factor, and optimizing a distance measurement formula of super-pixel segmentation to realize accurate segmentation of a complex surface area; and finally, carrying out classification detection on the super-pixel region features by utilizing a convolutional neural network, and identifying the type of the paint surface defect. According to the invention, through illumination correction, fluctuation factor construction, improved superpixel segmentation algorithm and convolutional neural network classification, automatic and accurate detection and classification of paint surface defects are realized.

The embodiment of the invention provides a display panel and a display device, the display panel comprises a substrate, an isolation structure, a light-emitting function layer, a touch control layer and a light-transmitting shielding layer, the isolation structure is arranged on one side of the substrate, and a plurality of limiting openings and a plurality of light-transmitting openings are defined by the isolation structure; the light-emitting functional layer comprises a plurality of light-emitting parts, and the light-emitting parts are at least partially arranged in the limiting openings; the touch layer is arranged on the side, away from the substrate, of the isolation structure; the light-transmitting shielding layer is arranged between the isolation structure and the touch control layer, and the light-transmitting shielding layer shields at least part of the light-transmitting opening in the direction from the touch control layer to the substrate. According to the display panel, the radio frequency interference between the touch control layer and the driving circuit layer can be avoided or reduced through the light-transmitting shielding layer, and the quality of images displayed by the display panel is improved.

The invention discloses a casting product appearance defect detection method and system based on machine vision, and particularly relates to the technical field of casting product defect detection. According to the method, casting materials of a production line are divided into different categories, the casting materials with abnormal absorptivity are identified, terahertz wave scanning is carried out on the casting materials, scattering images are obtained, and the stability of scattering point density distribution is evaluated; scanning by using a terahertz time-domain spectrometer, analyzing an amplitude fluctuation state of a time-domain signal and evaluating a waveform distortion degree; after comprehensive analysis, the quality of images captured by the imaging system is evaluated, the images are divided into different grades, defect detection is directly carried out on high-quality images, the quality images are centered, the accuracy of the images is analyzed, and the transmitting power and the scanning speed of terahertz imaging equipment are dynamically adjusted, so that the imaging quality is improved, and the accuracy and the efficiency of detection are remarkably improved; and the quality and safety of casting products are ensured, and the product quality problem and potential equipment faults caused by untimely defect detection are effectively avoided.

The invention discloses a visual detection system for an automobile door panel assembly, and relates to the technical field of automatic detection, and the visual detection system comprises the steps: installing a high-precision industrial camera, and carrying out the synchronous collection of the multi-view image data of an automobile door panel; preprocessing the acquired multi-view image data of the automobile door panel; carrying out detail enhancement on the preprocessed multi-view image data of the automobile door panel by adopting a super-resolution algorithm; integrating the enhanced multi-view image data of the automobile door panel into an automobile door panel panorama through binocular stereoscopic vision matching, and constructing an automobile door panel three-dimensional model; the automobile door panel three-dimensional model is compared with a standard automobile door panel three-dimensional model, and the buckle defect in the automobile door panel is recognized; and carrying out position marking and defect type analysis on the identified defect points, and generating an automobile door panel detection report according to the defect type analysis. According to the invention, details are enhanced by using a super-resolution algorithm, defects are accurately positioned by using a three-dimensional model, and the detection efficiency and accuracy are improved.

The invention relates to the field of aramid fiber band defect detection, and discloses an X-ray-based aramid fiber band defect online detection method and device. The method comprises the following steps: carrying out potential defect analysis on a physical characteristic image and a material parameter distribution diagram to obtain spatial distribution data of a potential defect region; physical characteristic quantitative analysis is carried out on the spatial distribution data of the potential defect area, a defect physical descriptor is obtained, and the defect physical descriptor comprises a density anomaly feature, a molecular chain orientation disorder degree and a thermal anomaly feature; and performing multi-dimensional feature extraction and weight distribution on the defect physical descriptor according to a preset material performance standard to obtain a quality score and a physical performance parameter of the aramid fiber band, and generating a defect detection report according to the quality score and the physical performance parameter. According to the invention, the comprehensiveness, accuracy and reliability of aramid fiber band defect detection are improved.

The invention discloses a generation method and system for controllable high-quality AI drawing picture description, and the method comprises the steps: carrying out the analysis and recognition of the drawing intention of a user through a natural language processing technology, and extracting key drawing elements from the drawing intention; the meanings of the elements and the relation between the elements are understood on the basis of a deep learning neural network model, and the drawing intention of the user is expanded and supplemented on the basis of a generative adversarial network, so that various drawing cue words in different directions are generated; and on the basis of the drawing cue word, generating an image according with the description of the drawing cue word by utilizing a diffusion model image generation algorithm, and obtaining various styles of images according with an expected quality standard by introducing an image quality evaluation system. The method has the advantages that user thinking divergence can be guided, creativity is stimulated, effective interaction is achieved, feedback optimization results are fully utilized, image quality is greatly improved, and generation diversity and flexibility are increased to adapt to various requirements and scenes.

The invention belongs to the field of image processing, and particularly discloses an imaging simulation method and device based on a point spread function and electronic equipment, and the method comprises the steps: building a light source model corresponding to an image shot by an optical system, and obtaining the point spread function of a point light source of the light source model after the action of the optical system; obtaining error point spread functions under different optical system models; performing weighted averaging on each error point spread function according to a preset weight to obtain a comprehensive point spread function; forming an array diagram by the comprehensive point spread function according to light distribution, and carrying out convolution on the array diagram and a shot image to obtain a degraded image simulating a blurring effect caused by aberration; and applying the distortion model of the optical system to the shot image to obtain a distortion-simulated deformation effect image, and realizing imaging simulation of the optical system based on the deformation effect image and the degraded image. According to the invention, the imaging accuracy and efficiency can be improved.

The invention discloses a mining area unmanned vehicle multi-mode competitive collaborative fusion sensing method, and relates to the technical field of fusion sensing, and the method comprises the following implementation steps: employing infrared thermal imaging and visible light image fusion to forward sense an obstacle, employing an ultrasonic radar and a laser infrared distance measurement sensor to sense a near-area obstacle, and employing a laser infrared distance measurement sensor to sense the near-area obstacle; whether an obstacle approaches or not is judged according to distance measurement information of sensors at four corners and four sides; a laser radar, a depth visible light imaging head and an inertial measurement unit are fused to perceive obstacles, and six modes are fused to construct a complete environment perception domain. According to the multi-mode competitive collaborative fusion perception method for the unmanned carrying vehicle in the mining area, posture changes are detected and tracked in real time, the angle of a vehicle steering engine is adjusted in advance, it is guaranteed that an image can accurately reflect an actual scene, the precision of state prediction of the carrying vehicle is further improved, and the safety and reliability of operation of the unmanned carrying vehicle in the mining area are improved.

The invention relates to the field of valve life prediction, in particular to a valve life prediction method and system based on big data analysis. The method comprises the following steps: acquiring a real-time operation monitoring image and a multi-dimensional operation monitoring parameter of the valve; performing valve assembly visual positioning on the valve real-time operation monitoring image, performing global brightness enhancement processing, and constructing a brightness enhanced valve image; performing frame-by-frame operation behavior optical flow tracking on the brightness enhanced valve image, and performing valve opening and closing period mining to obtain valve opening and closing cycle period characteristics; performing valve temperature change calculation and response time difference trend analysis on the multi-dimensional operation monitoring parameters to obtain a valve operation trend characteristic rule; and performing valve operation time sequence evolution simulation on the valve opening and closing cycle period characteristics based on the valve operation trend characteristic rule, and performing valve life cycle calculation so as to generate valve life cycle data. According to the invention, efficient and accurate valve life prediction is realized.

The invention provides an optical lens, which comprises seven lenses, and sequentially comprises a first lens with negative focal power, a second lens with negative focal power, a third lens with negative focal power, a fourth lens with negative focal power, a fifth lens with negative focal power and a sixth lens with negative focal power from an object side to an imaging surface, the second lens has negative focal power, the object side surface of the second lens is a concave surface near the optical axis, and the image side surface of the second lens is a concave surface; the object side surface and the image side surface of the third lens are concave surfaces; the object side surface and the image side surface of the fourth lens are convex surfaces; the object side surface of the fifth lens is a convex surface, and the image side surface of the fifth lens is a concave surface; the sixth lens has positive focal power, and the object side surface and the image side surface of the sixth lens are convex surfaces; the seventh lens has negative focal power, and the image side surface of the seventh lens is a concave surface near the optical axis; the curvature radius R3 of the object side surface of the second lens and the curvature radius R4 of the image side surface of the second lens satisfy 0.1 lt; (R3 + R4) / (R3-R4) lt; and 0.8. According to the optical lens provided by the invention, the imaging quality of the optical lens can be improved, the aberration is reduced, and the imaging quality of the optical lens is improved.

The invention relates to the technical field of synchronous acquisition, in particular to a multi-camera synchronous data acquisition method and system, and the method comprises the following steps: based on a real-time video stream of a camera, analyzing a video frame, recognizing a moving object in a differentiated video stream, and carrying out the recognition of the moving object in the differentiated video stream; and synchronously recording the occurrence time and position change of the moving object in the differentiated camera to obtain view angle change data. According to the invention, the angle and focal length of the camera are synchronously adjusted to cope with the view angle change of different scenes, and the trigger time of the camera is uniformly configured and is synchronously started through the network, so that the time deviation is reduced, the analysis of the synchronous trigger data set and the timestamp adjustment are synchronously triggered, and the synchronization between the cameras is optimized; the consistency and synchronism of the video streams obtained from different angles are ensured, the data transmission parameters are dynamically adjusted according to the network condition, the efficiency and synchronization precision in the data transmission process are improved, and the influence of network delay and bandwidth change is effectively dealt with.

The invention relates to an aluminum alloy handle surface defect detection method, device and equipment and a storage medium. The method comprises the following steps: respectively acquiring standard illumination original images and low illumination original images of a plurality of angles of the surface of the aluminum alloy handle, and preprocessing to obtain a preprocessed image pair of each angle; processing is carried out through three parallel convolution branches and residual connection, and an enhanced feature map of each angle is obtained; performing illumination compensation processing to obtain an illumination equalization image of each angle; performing optimization to obtain a de-noised image of each angle; and splicing and reconstructing the de-noised image of each angle to obtain a reconstructed surface image, and carrying out multi-scale feature fusion and surface defect detection on the reconstructed surface image to obtain a surface defect detection result. According to the method, the precision and recall rate of defect positioning on the surface of the aluminum alloy handle are improved.

The invention relates to the technical field of video compression processing. The invention provides a monitoring video compression processing method and device, and the method comprises the steps: extracting the video features of a to-be-processed image in a monitoring video in a target scene region through a video feature extraction model, and obtaining the feature vectors of a plurality of video features in different image regions; according to the obtained feature vectors of the various video features in the different image areas, performing following classification on the to-be-processed image to obtain classification results: image area classification, scene type classification and importance level classification; determining an initial compression ratio of different image areas in the to-be-processed image according to the classification result; decomposing the to-be-processed image into sub-images of different scales by adopting an image decomposition method based on wavelet transform, and determining an optimal decomposition layer number according to image quality; and determining a final compression ratio and a coding mode of different image areas according to the scale and content characteristics of the sub-images of the image to be processed. According to the method, the compression ratio is greatly improved on the premise that the image quality is not obviously reduced.

The invention discloses a synthetic aperture quantitative phase imaging method based on mixed digital holography and Fourier lamination, which comprises the following steps of: installing a programmable LED (light-emitting diode) array which is annularly arranged on an off-axis digital holography system for providing inclined illumination of angle transformation, obtaining accurate low-frequency information of an object which contains noise through digital holography, and taking the information as initial estimation; and then, object high frequency is reconstructed through a Fourier lamination algorithm based on intensity measurement, so that synthetic aperture quantitative phase imaging is realized. According to the invention, the accurate low frequency provided by holography is used as initial estimation, so that the accuracy of phase recovery and wavefront aberration reconstruction is ensured, a matched illumination condition does not need to be strictly met, and meanwhile, the number of intensity graphs required by Fourier lamination is reduced; the non-interferometric phase recovery method based on Fourier lamination improves the imaging resolution to an incoherent diffraction limit, and effectively solves the inherent speckle noise problem in digital holography.

The invention relates to the technical field of infant sleep nursing, solves the problem that in the prior art, the infant respiration frequency cannot be monitored in a non-contact mode, and provides a non-contact infant respiration monitoring method, device and equipment and a storage medium. The method comprises the following steps: acquiring multiple frames of real-time images in an infant sleep scene; monitoring the sleep state of the real-time image, and identifying a first target image when the infant enters the sleep state; performing image enhancement processing on the first target image, and outputting a second target image after respiratory movement enhancement; feature extraction is carried out on the second target image, and a target area related to infant respiration is recognized; according to the maximum likelihood criterion, the target area is processed, and infant respiration information is determined. The infant respiration is monitored in a non-contact mode, the accuracy of respiration frequency monitoring is improved through image collection, image enhancement processing and other modes, and intrusive monitoring on the infant is not needed.

The invention provides an optical lens, which comprises eight lenses from an object side to an imaging surface along an optical axis: a first lens with negative focal power, a second lens with negative focal power, a third lens with negative focal power, a fourth lens with negative focal power, a fifth lens with negative focal power and a sixth lens with negative focal power, the object side surface of the second lens is a concave surface, and the image side surface of the second lens is a convex surface; the object side surface of the third lens is a convex surface, and the image side surface of the third lens is a concave surface; the object side surface and the image side surface of the fourth lens are convex surfaces; the fifth lens has positive focal power, and the image side surface of the fifth lens is a convex surface; the sixth lens has positive focal power, and the image side surface of the sixth lens is a convex surface; the object side surface and the image side surface of the seventh lens are concave surfaces; and the object side surface and the image side surface of the eighth lens are convex surfaces. According to the optical lens provided by the invention, the imaging quality of the optical lens can be improved, the aberration is reduced, and the imaging quality of the optical lens is improved.

The invention discloses an intelligent detection system for the surface quality of a copper foil. The intelligent detection system comprises an acquisition module, a construction module and a detection module, the acquisition module is used for acquiring a multi-angle multi-spectral image of the surface of the copper foil and preprocessing the multi-angle multi-spectral image; the construction module is used for carrying out automatic identification and positioning on the surface of the copper foil by utilizing a defect detection algorithm based on deep learning based on the preprocessed multi-angle multispectral image, and constructing a copper foil surface defect identification model; and the detection module is used for comprehensively detecting the surface defects of the copper foil to be detected by using the copper foil surface defect identification model. According to the invention, comprehensive and accurate detection of surface defects of the copper foil can be realized, and the quality control level of the copper foil is improved.

The invention relates to an underwater image enhancement method and device based on generative adversarial network multi-scale fusion, and the method comprises the steps: obtaining an original underwater image, and carrying out the preprocessing of the original underwater image; inputting a preprocessing result into an underwater image enhancement model based on generative adversarial network multi-scale fusion to obtain an enhanced underwater image; the underwater image enhancement model comprises a generator and a discriminator, and the generator is obtained by replacing the last downsampled convolution module in an encoder of the U-Net network with a multi-scale feature fusion module; and the multi-scale feature fusion module is used for extracting multi-scale fusion features by adopting multi-scale convolution, residual connection, dense connection and a global attention mechanism. The method can adapt to different underwater scenes and enhance the image quality, the color cast and brightness recovery of the underwater image are improved, the robustness of the image is kept, and color distortion and noise artifacts are avoided.

The invention provides an optical imaging lens, the optical imaging lens has eight lenses with focal power, and the optical imaging lens comprises a lens group, a spacing element group and a lens barrel group, the effective focal length f7 of the seventh lens and the interval EP67 between the sixth spacing element and the seventh spacing element in the direction of the optical axis of the optical imaging lens meet the following condition: f7 / EP67 is more than or equal to-8.25 and less than or equal to-6.82; the curvature radius R15 of the object side face of the eighth lens and the inner diameter d7m of the image side face of the seventh spacing element meet the condition that R15 / d7m is larger than or equal to 1.31 and smaller than or equal to 2.28. The multi-group optical imaging lens solves the problems that in the prior art, the rear end forming yield of the multi-group optical imaging lens is low, and stray light is serious.

The invention discloses an SAR imaging method and system based on sparse and low-rank decomposition, and a readable storage medium. The imaging method comprises the following steps: collecting an SAR image and two-dimensional echo data, and preprocessing the two-dimensional echo data to obtain a target reflectivity; and based on the linear relationship between the two-dimensional echo data and the target reflectivity, decomposing the SAR image by using a preset method to obtain a sparse component and a low-rank component. A decomposition result is iteratively updated through a preset optimization algorithm, the precision is improved until errors converge, and a sparse and low-rank decomposition algorithm expression is obtained; and solving a preset SAR imaging model by using the sparse and low-rank decomposition algorithm expression, outputting an SAR imaging result, and finally outputting the SAR imaging result. According to the invention, the complexity of SAR imaging can be effectively reduced, and the imaging efficiency is improved. Even if the data sampling rate is reduced, the accuracy of image reconstruction can be ensured.

The invention provides a three-dimensional reconstruction method, device and equipment for an image in a capsule endoscope cavity, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a first image set shot by the capsule endoscope in a cavity; performing frame rate enhancement on the first image set based on a pre-trained depth perception model to form a second image set; rejecting unqualified images in the second image set based on corner detection of each second image in the second image set to form a third image set; and recognizing three-dimensional scene information of the capsule endoscope based on the third image set, and performing three-dimensional reconstruction on the cavity based on the three-dimensional scene information. According to the method, the three-dimensional image reconstruction quality can be improved, and the three-dimensional reconstruction process does not need to depend on additional sensor equipment.

The invention is suitable for the technical field of naked eye 3D, and provides a naked eye 3D interaction method and system based on OSG, and the method comprises the following steps: loading an OSG library and a deep learning model, loading a 3D model, a map and a light source through OSG, and obtaining an OSG scene; collecting interaction data of a user in real time, inputting the interaction data into the deep learning model, and performing real-time analysis; outputting the visual angle parameter and the model parameter obtained through analysis, and adjusting the camera position and the model posture in the OSG scene according to the visual angle parameter and the model parameter; according to the optimized OSG scene, utilizing a computer vision technology to generate a parallax image, and utilizing a deep learning algorithm to optimize the parallax image so as to improve the reality sense and the stereoscopic sense of the 3D effect; and synthesizing the optimized parallax image into a naked eye 3D image. According to the invention, real-time and intelligent adjustment can be carried out according to the actual interaction behavior of the user, and the reality sense and stereoscopic impression of the 3D effect are strong.

The invention discloses a large-aperture three-waveband integrated optical system, and solves the technical problem that an existing multi-waveband imaging system is large in size and weight. The optical system comprises a main reflector, a first spectrum spectroscope, a first folding-axis mirror, a second folding-axis mirror, a second spectrum spectroscope, a visible light optical unit, a short-wave optical unit, a third folding-axis mirror and a medium-wave optical unit. Wherein the main reflecting mirror, the first spectrum spectroscope, the first folding-axis mirror, the second folding-axis mirror, the second spectrum spectroscope and the visible light optical unit form a visible light optical system for receiving visible light beams; the main reflecting mirror, the first spectrum spectroscope, the first folding-axis mirror, the second folding-axis mirror, the second spectrum spectroscope and the short-wave optical unit form a short-wave infrared optical system for receiving short-wave infrared light beams; the main reflector, the first spectrum spectroscope, the third folding-axis mirror and the medium-wave optical unit form a medium-wave infrared optical system to receive medium-wave infrared beams. The optical system adopts a modular design and is high in integration level.