675 results about "Image correction" patented technology

The invention relates to the technical field of image correction, in particular to a correction method and system for a low-distortion image of a panoramic camera. The method comprises the following steps: based on a second-hand vehicle scene acquisition demand, acquiring a panoramic image and executing basic preprocessing to obtain a preprocessed panoramic image; calling a lens distortion coefficient, analyzing a corresponding distortion parameter in combination with a checkerboard calibration algorithm, and generating a polar region distortion distribution diagram; carrying out segmentation and grid reconstruction on a polar region of the preprocessed panoramic image, and executing distortion elimination and geometric reduction to obtain a preliminary correction image; fine tuning is carried out on the pixel position of the preliminary correction image, color fault and edge dislocation of a multi-lens splicing seam are eliminated, and an optimized correction image is obtained; executing second-hand car scene customized enhancement processing on the optimized correction image to obtain an enhanced correction image; and automatically checking the enhanced correction image, and outputting a low-distortion final correction image. According to the invention, the correction efficiency of the panoramic low-distortion image of the second-hand vehicle can be improved.

The invention belongs to the technical field of image processing, and particularly relates to a cable shielding layer weaving density detection system based on image analysis, which comprises an image correction module, a frequency domain analysis module, a self-adaptive filtering module and a density defect detection module, obtaining the maximum peak point of the centralized complex frequency spectrum; constructing a band elimination filter according to the maximum peak point, filtering the centralized complex frequency spectrum through the band elimination filter to obtain a filtered complex frequency spectrum, executing inverse Fourier transform to obtain a filtered space image, and binarizing the filtered space image through a threshold segmentation algorithm to obtain a pore mask; the porosity is obtained according to the ratio of the non-pore pixel points to the total number of the pixel points; and judging the weaving defect of the shielding layer according to the porosity. According to the method, the strong periodic signals of the weaving textures are automatically identified and filtered in the frequency domain, so that non-periodic defects such as pores are effectively highlighted, the technical problem that periodic textures cover defect signals is solved, and the accuracy of defect detection is improved.

The invention relates to the technical field of image processing, and discloses an image correction method and system for binocular diffraction AR intelligent glasses. The method comprises the following steps: calculating a dispersion response parameter of a diffractive optical element in the AR glasses in a visible light wave band; respectively carrying out dispersion distortion detection on the left eye image and the right eye image in the AR glasses to obtain a left eye dispersion distortion feature and a right eye dispersion distortion feature; interpupillary distance change data of a user is obtained, effective aperture offset calculation is carried out on the diffractive optical element, and interpupillary distance compensation parameters are obtained; performing coupling calculation on the left eye dispersion distortion feature, the right eye dispersion distortion feature and the interpupillary distance compensation parameter to obtain a binocular dispersion correction matrix; and performing dispersion compensation on the left eye image and the right eye image to obtain a dispersion correction image, and outputting the dispersion correction image to a display module of the AR glasses. The method can respond to the pupil distance change of the user in real time, automatically adjust dispersion correction parameters, realize accurate and comprehensive dispersion correction, and ensure the stability of dispersion correction performance.

The invention discloses a farmland soil moisture content real-time surveying system and method based on unmanned aerial vehicle multispectral imaging. The method comprises the steps that multispectral sensor parameters are configured, and an unmanned aerial vehicle flight scanning path is planned; a space coordinate system and time synchronization mechanism is established, and high-precision positioning and time synchronization are achieved; collecting and calibrating visible light, near-infrared and short-wave infrared original image data to form a spectral signal set; calculating an image signal-to-noise ratio, screening a low-quality image, and executing noise suppression; extracting timestamp information from the suppressed image, performing spatial positioning, and establishing a spatial corresponding relation database; calculating time synchronization precision and performing image correction; evaluating the spatial resolution according to the distortion coefficient; calculating a comprehensive quality parameter, comparing the comprehensive quality parameter with a threshold value, and obtaining a soil moisture content distribution parameter when a condition is met; a farmland moisture state map is generated, partition marking is carried out according to a multi-level standard, a monitoring result is output, and high-precision and real-time monitoring of the farmland soil moisture content is achieved.

The invention discloses an underwater target identification and positioning method based on fusion of a physical model and deep learning, and belongs to the technical field of computer vision, and the method comprises the steps: carrying out the transmissivity estimation and physical restoration of a collected underwater monocular image according to an underwater light propagation model, and carrying out the image correction; key feature points are extracted, and high-quality matching point pairs are screened in combination with the joint similarity; further deriving a basic matrix through the high-quality matching point pairs meeting the epipolar geometric constraint relation, solving an essential matrix, and obtaining relative attitude parameters between the cameras in combination with weighted re-projection-LM optimization; and finally, refraction correction triangulation is carried out according to the Snell's law, pixel-level fusion is carried out after scale normalization and space alignment are carried out on the refraction correction triangulation and dense depth output by the MiDaS, three-dimensional space coordinates of the target are inverted, and high-precision recognition and positioning of the underwater target are achieved. The system is light in structure, efficient in calculation, suitable for being integrated on various underwater autonomous or remote control robot platforms and used for tasks such as target recognition, tracking and positioning.

The invention relates to the technical field of electron microscopic imaging, in particular to a correction method and device for geometric distortion in scanning electron microscope measurement, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a scanning electron microscope image of a standard sample; calculating an orthogonal included angle parameter and a length-width ratio parameter of the scanning electron microscope image; iteratively adjusting the angle correction parameter and the gain correction parameter under a fixed scanning rotation angle until a preset local calibration constraint condition is met; verifying the orthogonal included angle parameter and the length-width ratio parameter under each scanning rotation angle; and when the orthogonality included angle parameter and the length-width ratio parameter do not meet a preset global stability constraint condition, adjusting a relative gain coefficient, and re-executing calibration verification under each scanning rotation angle until a full-angle correction requirement is met. By adopting the method, high precision and stability of imaging correction can be kept in a full-angle range.

A system including: a multiscopic display; camera(s) that is / are positioned such that a display area of the multiscopic display lies at least partially within a field of view of the camera(s); and processor(s) configured to: display an image via the multiscopic display, whilst capturing image(s) of the display area using the camera(s); rectify the captured image(s) of the display area, to represent the display area only; generate a frequency-domain representation of the displayed image and a frequency-domain representation of the captured image(s) of the display area after rectifying; and determine a correct value of parameter(s) of a multiscopic optical element of the multiscopic display, based on the frequency-domain representation of the displayed image and the frequency-domain representation of the captured image(s) of the display area.

The invention discloses a civil engineering foundation pit deformation monitoring system based on image analysis, and relates to the technical field of foundation pit deformation monitoring, and the system collects and enhances a multi-modal image, eliminates interference, corrects image distortion, tracks key points, constructs a panoramic image, generates a point cloud, extracts deformation features, and constructs a deformation map. The method comprises the following steps: identifying a construction disturbance event, constructing a deformation chain and performing causal reasoning, identifying a high-risk area, predicting a deformation trend and outputting an early warning, displaying a monitoring result, collecting user feedback and synchronizing system data. A deformation track is accurately extracted through image correction and key point tracking, a three-dimensional deformation map is constructed in combination with point cloud modeling, causal analysis is carried out based on construction disturbance and a deformation path, a high-risk area is predicted and identified by using a time sequence trend, an early warning is given out, user feedback collection and result synchronization are supported, and monitoring intelligence and response efficiency are improved.

The invention discloses a stay cable vibration recognition method and system based on unmanned aerial vehicle video and image correction. The method comprises the steps that S100, a dynamic response video of a stay cable under natural excitation is shot through an unmanned aerial vehicle; s200, performing semantic segmentation on a stay cable region in the video image to obtain a clear stay cable pixel region; s300, pixel displacement tracking is carried out on the inhaul cable target area and the peripheral fixed reference point area, and time sequence displacement data containing inhaul cable vibration response and unmanned aerial vehicle disturbance components are extracted; s400, geometric correction of the image sequence is realized, and overall jitter caused by attitude change of the unmanned aerial vehicle is eliminated; s500, extracting an intrinsic mode component; identifying a common-mode disturbance component by comparing modal features of the two, and stripping from the inhaul cable response; and S600, carrying out identification and early warning on abnormal vibration behaviors such as loosening, strand breaking and vibration excitation abnormity. And high-precision and automatic monitoring of the vibration state of the stay cable is realized through a technical closed loop from video acquisition to abnormal early warning.

The invention discloses an intelligent pipe surface defect detection method based on image recognition. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data of a to-be-detected pipe fitting, and calculating Gaussian curvature and normal vector of each point; dividing the surface of the pipe fitting into a plurality of geometric homogeneous areas based on Gaussian curvature difference; a multi-view original image is collected in each area, a local equidistant mapping function is constructed by using a normal vector and Gaussian curvature, and the image is corrected and expanded into a two-dimensional plane image; illumination normalization and learnable zooming and translation operations are carried out on the expanded image; inputting the processed image into a defect detection neural network comprising a geometric attention module and a context sensing decoder, and outputting a pixel-level defect probability graph; and finally, projecting a defect result back to a three-dimensional space through an inverse mapping function. The method effectively overcomes imaging distortion and identification difficulty caused by complex geometrical shapes of the pipe fittings, and is suitable for high-precision surface defect detection of various special-shaped pipe fittings.

The invention provides a soft bag detection process monitoring method and system and a soft bag light inspection conveying device, and relates to the technical field of soft bag detection monitoring, and the soft bag detection process monitoring method comprises the steps of image feature optimization, camera triggering time sequence optimization, image correction and soft bag sorting. According to the method, the soft bag images at different visual angles are obtained, whether image feature optimization is executed to correct the difference between the cameras is judged based on the image features of the soft bag images, then the soft bag is delivered to the particle foreign matter detection station, the sudden stop operation is executed, and the sudden stop soft bag image is obtained through the industrial camera after the soft bag is suddenly stopped. Whether camera triggering time sequence optimization is executed or not is judged based on time sequence parameters shot by an industrial camera, then whether geometric distortion correction is executed or not is judged based on consistency parameters of soft bag postures in sudden stop soft bag images, and finally whether emergency response is triggered or not is judged according to soft bag detection time consumption of all soft bags, so that the monitoring reliability of the soft bag detection process is improved, and the detection efficiency is improved. The problem of low monitoring reliability in the soft bag detection process in the prior art is solved.

The invention provides a cold region greenhouse multispectral visual positioning picking method and system. The method comprises the following steps: synchronously acquiring an RGB image, an NIR image, greenhouse environment temperature and humidity data and depth information of a target; based on the RGB image, the NIR image and the environment data, image correction is carried out through an image correction network, and a sharpened RGB image is output; inputting the corrected RGB image, NIR image and depth image into a maturity identification model for processing, and outputting the maturity grade and three-dimensional space coordinates of the fruit; based on the three-dimensional coordinates and maturity information of the fruits and a pre-constructed environment three-dimensional point cloud map, a collision-free optimal picking path is planned for a mechanical arm; the mechanical arm is controlled to execute the picking action, the success rate of the picking result and the recognition confidence coefficient serve as feedback signals, and online fine adjustment is conducted on parameters of the image correction network and the maturity recognition model; the problems of low picking efficiency, low precision and high damage rate of the greenhouse in the cold region are solved.

The invention discloses a high-rotating-speed hydraulic generator rotor bolt looseness detection method and system, belongs to the technical field of hydropower station high-rotating-speed rotating mechanical equipment detection, and aims at solving the technical problems that hydraulic generator rotor bolt looseness detection is low in efficiency and poor in precision, real-time monitoring cannot be achieved, and safe and stable operation of a hydropower station is seriously affected. According to the invention, the image of the rotor bolt area is collected, and the image is accurately processed by using an image correction algorithm, so that image distortion is eliminated; thirdly, fusing an improved YOLOv8 target detection technology, a DeepLabV3 + semantic segmentation technology and a DeepSort multi-target tracking technology, and accurately extracting angle features of the bolt marking line; and then, a looseness identification objective function is constructed based on the angle change of the bolt, the function is solved in real time, and a detection result is output. According to the invention, high-precision, non-contact and real-time bolt looseness detection is realized, the detection efficiency and reliability are effectively improved, and a solid guarantee is provided for safe operation of a hydropower station.

The present application relates to a projection picture correction method, and a projection device. The method comprises: acquiring a captured image captured when a projection device projects a preset image, wherein the preset image comprises a positioning mark and a correction mark, the positioning mark is used for positioning the preset image, and the correction mark is used for performing precision correction; and determining an image correction parameter on the basis of the positioning mark and the correction mark, and correcting a projection picture on the basis of the image correction parameter. By means of the present application, accurate coordinates of a projector screen in a projection device coordinate system required for projection picture correction can be determined on the basis of a captured image, such that the difficulty in projection picture correction can be reduced, and the efficiency and accuracy of projection picture correction can also be improved.

The invention relates to the technical field of ship draft measurement, and provides a ship draft dynamic detection method and system based on multi-modal fusion. The method comprises the following steps: acquiring a multi-modal ship image and / or video to obtain a ship visible light image and a ship near-infrared image; performing feature level fusion on the ship visible light image and the ship near-infrared image to obtain a fused image; performing key region detection on the fused image by using a pre-trained waterline detection model adopting YOLOv11, and marking and / or cutting a key waterline region in the fused image to obtain a target image; a SegFormer network added with an NAM attention mechanism is used for carrying out ship body and water body segmentation on the target image subjected to image correction, and ship body character features and waterline pixel features are obtained through edge detection; and constructing a visual ranging model containing character coordinates based on the hull character features, and inputting the waterline pixel features into the visual ranging model to obtain a ship draft detection result.

The invention provides a CT metal artifact correction algorithm based on combination of double-domain diffusion and wavelet attention, and relates to the technical field of image correction. According to the method, the accuracy and effectiveness of CT metal artifact correction are improved by fusing the wavelet attention mechanism and the double-domain diffusion model, and the accuracy and effectiveness of CT metal artifact correction are improved by means of targeted extraction of the wavelet attention module on the high-frequency component of the image and quantitative analysis of the gradient direction consistency index and the curvature entropy. Accurate distinguishing of real edges and metal artifact fragments is achieved, and the problem of structure loss caused by confusion of edges and artifacts in a traditional method is effectively solved. Besides, on the basis of the design of edge geometric attribute dynamic distribution diffusion parameters, a small convolution kernel and a slow step length are adopted for a high-curvature edge to reserve a fine structure, and a large convolution kernel and a fast step length are adopted for an artifact area to strengthen the suppression effect, so that the artifact removal efficiency is improved, and the damage of excessive smoothness to key edge features is avoided; and the balance between the local fine structure and the global smooth demand is realized.

The invention relates to the technical field of image processing, and discloses a method and system for switching backgrounds in batches to form a new image based on a convolutional neural network, and the method comprises the steps: carrying out the analysis of a cartoon image, obtaining a foreground segmentation mask and a foreground material feature tensor, carrying out the analysis of a target background image, and obtaining a background illumination feature vector; dynamic light and shadow interaction between a cartoon foreground and a new background is simulated by constructing a double-medium light interaction field, fine fusion of an edge region is realized in combination with an edge transmission matrix, the problem of visual splitting caused by light and shadow interaction deficiency in background switching of a traditional convolutional neural network is solved, and meanwhile, through a light and shadow residual image correction mechanism, the dynamic light and shadow interaction between the cartoon foreground and the new background is corrected. The illumination consistency is further optimized, the fusion matching degree of the cartoon image and various real or virtual scenes is remarkably improved, the diversified marketing scenes can be efficiently adapted through a batch processing mode, and the batch replacement requirement is met while the visual naturalness is guaranteed.

The embodiment of the invention provides an image correction method and an image correction system, and the method comprises the steps: obtaining an initial question graph, a vector graph marking result, an answer text, and a rendering question graph, the vector graph marking result is obtained by performing vector graph marking on a target graph element in the initial question graph based on the answer text, and the rendered question graph is obtained by rendering the target graph element based on the vector graph marking result; comparing the initial question graph with the rendered question graph, and extracting a target graph element; based on the answer text, performing consistency verification on the target graphic element to obtain a verification result; and based on the verification result, adjusting the rendering question graph to obtain an image output result. Errors existing in target graphic elements are actively and efficiently perceived, and the rendered question graph is adjusted based on the verification result, so that the blank that an existing automatic drawing system lacks a drawing quality control mechanism is filled.

The application belongs to the technical field of image processing, and particularly relates to a cable shielding layer weaving density detection system based on image analysis, which comprises an image correction module, a frequency domain analysis module, an adaptive filtering module and a density defect detection module; a maximum peak point of a centralized complex frequency spectrum is obtained; a band-stop filter is constructed according to the maximum peak point, and the centralized complex frequency spectrum is filtered through the band-stop filter to obtain a filtered complex frequency spectrum and perform inverse Fourier transform to obtain a filtered spatial image, and the filtered spatial image is binarized through a threshold segmentation algorithm to obtain a pore mask; a porosity is obtained according to the ratio of non-pore pixel points to the total number of pixel points; and the shielding layer weaving defect is judged according to the porosity. The application automatically identifies and filters the strong periodicity signal of the weaving texture in the frequency domain, effectively highlights the non-periodic defects such as pores, solves the technical problem that the periodic texture covers the defect signal, and improves the accuracy of defect detection.

The invention provides a machining part optical detection method and system based on industrial vision, and relates to the technical field of industrial vision detection. Comparing the optical response of each region in the image data with a pre-stored standard spectral response characteristic of a typical pollutant, and identifying the existing region and type of the pollutant; according to the identified pollutant information, influence distribution information is generated; according to the influence distribution information, carrying out real-time image correction compensation processing on the pollutant existence area in the image data to obtain target image data; the three-dimensional shape data of the workpiece is obtained based on the target image data and compared with the preset three-dimensional digital model to judge the quality of the workpiece, the problems that in the prior art, traditional manual quality inspection is low in efficiency and high in misjudgment rate are effectively solved, and the interference of by-products generated in the machining process on the accuracy of an optical detection system is overcome.

A surface defect detection method for optically detecting a surface defect in a strip-shaped body includes an image acquisition step of detecting reflected light from the strip-shaped body obtained by illuminating a surface of the strip-shaped body and imaging while relatively scanning the surface of the strip-shaped body to acquire a plurality of images including the surface of the strip-shaped body, an average image calculation step of calculating an average image of the acquired images, an image correction step of performing shading correction on each acquired image using the average image to obtain corrected images, and a defect detection step of detecting a surface defect in the strip-shaped body based on the corrected images. The average image calculation step includes recognizing an inspection target region in which the strip-shaped body is located in each image and contributing to the average image only for pixels in the inspection target region.

The invention relates to a vehicle residual value prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: processing vehicle non-image data of a vehicle, obtaining to-be-processed data features, inputting the to-be-processed data features into at least two basic models, obtaining at least two initial prediction results, and inputting the initial prediction results into an integrated model to obtain a basic residual value prediction result; inputting vehicle image data of a vehicle into the visual language large model to obtain an image correction factor feature vector; and splicing the basic residual value prediction result and the image correction factor feature vector as an input feature vector, inputting the input feature vector into a regression model, and processing to obtain a target residual value prediction result of the vehicle. According to the technical scheme, the prediction results of the multiple basic models are fused through the integrated model to obtain the basic residual value prediction result, the basic residual value prediction result and the image correction factor feature vector are spliced and then input into the regression model for final prediction, the target residual value prediction result is obtained, and the vehicle residual value prediction accuracy is improved.

The invention relates to a pointer instrument reading method and system combining image correction and multi-feature recognition, and the method comprises the steps: obtaining an original image, positioning and cutting a dial region, and obtaining a to-be-processed image; calling the reference dial image, performing feature point identification, and performing perspective transformation on the to-be-corrected image to obtain a standard image; carrying out feature recognition on the standard image, wherein necessary categories comprise a dial plate center point, a half pointer, scale marks and numbers; obtaining pointer tip coordinates based on the half pointer and the dial center point; clustering the identified digits to obtain a plurality of scale values; correlating corresponding scale marks and angle information for each scale value, and performing ascending order arrangement; and screening out two scale marks closest to the tip of the pointer, calculating the relative angle between the tip of the pointer and the two scale marks, and carrying out interpolation to obtain the instrument reading. According to the pointer type instrument reading method and system, the accuracy of the instrument at different inclination angles can be effectively improved.

The invention provides a train detection image correction positioning method and device based on depth registration and mapping comparison, which can solve the problems of low matching precision, unstable mapping and lack of adaptive anomaly correction capability, and comprises the following steps: obtaining a template image and a current image to be detected; segmenting the current image into a plurality of sliding windows with overlapped areas by adopting a sliding window recursive registration mechanism, and calculating a local registration matrix for each sliding window; generating a global registration matrix based on the local registration matrix of the plurality of sliding windows; using the global registration matrix to map the coordinates of the template detection item points to the current image coordinate system to obtain the expected coordinates of the corresponding mapping item points; obtaining a detection result of the target detection model on the current image, and obtaining detection coordinates of actual detection item points; and matching the expected coordinate of the mapping item point with the detection coordinate of the actual detection item point, and determining the final positioning coordinate of the detection item point according to a preset correction rule.

The invention discloses an ancient book document image correction method based on text recovery and reconstruction, which relates to the technical field of computers, and comprises the following steps: constructing a text recovery model based on a probability diffusion model, a residual block and a time embedding technology; constructing a loss function, introducing a training strategy of frequency domain decomposition, and training the text recovery model together with the loss function to obtain a trained text recovery model; inputting an ancient book document image into the model to obtain a preliminarily recovered ancient book document image; constructing a document image correction model based on an Enet network and a Tnet network, training the Enet network by adopting supervised training and weak supervised training, and training the Tnet network by adopting self-supervised training to obtain a trained document image correction model; inputting the preliminarily recovered ancient book document image into the trained document image correction model to obtain a corrected ancient book document image; according to the method, the text of the ancient book image can be clearly reconstructed, the reconstructed text deformation features are learned, and the correction performance is effectively improved.

The invention relates to the technical field of image correction, and particularly discloses an X-ray image correction method and device based on deep learning and terminal equipment. The method comprises the following steps: acquiring a pulse full-time original image and an energy spectrum time sequence signal, and extracting to obtain a distortion core feature; a distortion transmission path is extracted based on the distortion core features, a reverse mapping model is constructed to output distortion magnification times, and a calibration coefficient matrix is established; screening a core energy interval and a corresponding core trace element peak; the calibration direction is bidirectionally constrained through a bimodal linkage calibration mechanism, and a corrected signal and image are output; the device comprises a feature extraction module, a distortion calibration module, a core extraction module and a linkage calibration module. The terminal device executes the steps of the program implementation method through the processor, and the storage medium stores the corresponding computer program. According to the invention, the trace element peak detection precision and calibration stability are improved, and the reliability of X-ray detection is enhanced.

This application relates to a hyperspectral image correction method, apparatus, computer device, and storage medium. The method includes: acquiring a hyperspectral image; the hyperspectral image containing multiple pixels; extracting lateral spectral features of the hyperspectral image using a feature extraction network; determining distortion information of each lateral spectral feature based on the lateral spectral features and an image distortion algorithm; determining the distortion distribution of the hyperspectral image based on the distortion information and a distortion distribution algorithm; determining the positional deviation value of each pixel in the hyperspectral image based on the distortion distribution and the hyperspectral image; and adjusting each pixel in the hyperspectral image using the positional deviation values ​​to obtain a corrected hyperspectral image. This method improves the correction effect of hyperspectral images.

The invention discloses a six-axis X-ray automobile casting defect three-dimensional reconstruction detection method, relates to the technical field of intelligent manufacturing, and solves the problems of defect misjudgment caused by scattering artifacts in a non-uniform wall thickness or multi-cavity casting and reconstruction distortion caused by asynchronous moving images under high-speed scanning in the existing X-ray detection. According to the scheme, a ray configuration vector based on casting structure attributes is constructed, and is bound to a six-axis trajectory node in a path planning stage; generating an exposure control frame with synchronous postures and carrying out equipment response delay correction; image correction is carried out through a scattering inversion model, spatial offset is corrected through a coordinate mapping function in the back projection process, and meanwhile structural consistency constraint is applied to a reconstructed body; according to the method, the defect identification precision and the three-dimensional reconstruction quality of the casting with the complex structure under high-speed scanning are remarkably improved.

The invention discloses an archive informatization integration method and system based on deep learning, and relates to the technical field of computers. The method comprises the following steps: receiving a paper archive digital scanning image, paper archive layout structure information, electronic archive text data, photo archive image data, an audio and video archive and a transfer text thereof, external business system structured business data, archive original metadata fields, user identity information and user role information; and submitting a query request and a cross-modal query request by a user. According to the method, unified format analysis, text standardization, image correction and metadata verification are performed on heterogeneous archive data from different sources and different formats, so that the problems of fragmentation and non-structuring of the archive data are solved, and standardization and high-quality input of the archive data are realized; performing semantic coding on the multi-modal archive data by using a deep learning technology to generate an archive semantic vector set;

The invention discloses an ancient document image correction method based on super-resolution reconstruction. The method comprises the following steps: acquiring a real-degraded image data set and a twisted text-normal text pairing data set; training based on the real-degraded image data set and the twisted text-normal text pairing data set to obtain a trained super-resolution reconstruction model and a trained document image correction model; and inputting an original ancient book document image into the super-resolution reconstruction model to obtain a high-resolution ancient book image, and then inputting the high-resolution ancient book image into the document image correction model to obtain a corrected ancient book image. According to the method, the precision of document deformation correction of the ancient document image with document blurring, paper deformation and large background interference can be effectively improved, and powerful technical support is provided for digital protection of the ancient document image.