150 results about "Gaussian pyramid" patented technology

The invention discloses a glass lens surface scratch detection method and system, relates to the technical field of precision optical detection, and aims to solve the problems of scratch false detection, leak detection and poor algorithm adaptability caused by interference fringes, noise coupling and poor form adaptability in a high-reflection / complex coating process scene in the prior art. According to the scheme, an orthogonal polarization state composite light field is generated based on a multi-angle polarization light source array and a near-infrared compensation light source, and candidate regions are extracted through dynamic threshold segmentation and a direction gradient tensor matrix; gaussian pyramid multi-scale feature fusion and refraction angle consistency verification are utilized to eliminate artifact interference; constructing a direction constraint convolution kernel group to decompose scratches and background textures, and dynamically allocating computing resources in combination with a cascade network; feeding back closed-loop calibration light source wavelength and convolution kernel parameters in real time through coating parameters; according to the method, the precision and robustness of high-reflectivity surface scratch detection are remarkably improved, and meanwhile, the requirements for high-resolution image processing and real-time performance in a high-speed production line are balanced.

The invention discloses a data line surface defect rapid nondestructive detection method based on intelligent image recognition, relates to the technical field of image data processing, and aims to solve the technical problems of difficult defect feature separation and low detection accuracy under complex weaving texture noise interference, and the method comprises the following steps: S1, collecting a data line image and carrying out graying processing; s2, constructing a multi-scale image pyramid, accurately segmenting the image by adopting a self-adaptive threshold algorithm, and realizing rapid detection of surface defects of the data line in combination with Hough transform; s3, frequency domain separation of weaving texture and background noise is realized through Fourier transform, interference is filtered out in combination with a band-pass filtering technology, and then the defect contrast is enhanced through histogram equalization; according to the method, the three-layer Gaussian pyramid is constructed for multi-scale decomposition, and the band-pass filtering technology is combined, so that the weaving texture and the defect signal are effectively separated, the detection accuracy is greatly improved, and the problems of missing detection and misjudgment caused by frequency characteristic confusion are thoroughly solved.

The invention discloses a real-time puzzle method based on monocular SLAM (Simultaneous Localization and Mapping) fused with GPS (Global Positioning System), which is characterized in that an unmanned aerial vehicle is utilized to load a camera to shoot an image sequence under the camera and a GPS module is utilized to receive latitude and longitude signals, and high-precision real-time tracking and map construction of an outdoor large scene are realized by optimizing a feature extraction and matching algorithm of monocular ORBSLA3. Generating a sparse point cloud map and a position attitude of the key frame; a GPS module signal is subjected to UTM coordinate conversion, and fusion of the GPS module signal and key frame information is realized through linear search of a difference value; the key frame position attitude is converted to global UTM geographic coordinates through fusion transformation; overlapping region fusion is carried out on key frames with close spatial positions by using a Laplacian Gaussian pyramid to obtain an accurate panorama; according to the method, large-scene image splicing can be realized, the overlapping region splicing effect is good, the splicing real-time performance is good, and the method is suitable for application occasion requirements in multiple fields of outdoor aerial photography, aerial survey and the like.

The invention provides a low-light large dynamic image enhancement method based on dynamic weight and pyramid fusion. The low-light large dynamic image enhancement method is suitable for complex illumination imaging in security monitoring. According to the method, the global weight and the detail weight of an image are obtained according to the global average brightness, the local brightness and the gradient of an image sequence, and the total weight is obtained through weighting. And the halo phenomenon easily occurring in the fusion process is reduced through guide filtering. And combining Gaussian pyramid and ratio pyramid decomposition and weighting to obtain a final fusion image. According to the method, the problems of detail loss and limited dynamic range in the low-light scene are effectively solved. Experiments show that the method is superior to the prior art in information entropy, average gradient, edge strength, spatial frequency and other key indexes. The technology has an all-time adaptive capability, is suitable for the fields of intelligent transportation, urban security and protection, low-illumination monitoring and the like, and has a wide market prospect.

The invention provides a three-dimensional map construction method and device, computer equipment and a storage medium, and belongs to the field of image processing.The method comprises the steps that image information is obtained in real time; estimating pose information of the target camera; performing sub-mapping processing on the image information to obtain multiple pieces of sub-map information; performing dense mapping on each piece of sub-map information by adopting Gaussian sputtering, generating Gaussian point cloud corresponding to each piece of sub-map information, and generating a region mask corresponding to each piece of sub-map information in combination with the image information; determining a high color gradient region and a sparse region according to the region mask; constructing a multi-layer Gaussian pyramid of the key frame based on the high color gradient region and the sparse region; and reducing the number of layers of the pyramid through iterative training, completing rendering of the sub-map information, and obtaining a three-dimensional map corresponding to the image information. Therefore, computing resources are saved, the data processing efficiency and the map rendering speed are improved, and the SLAM performance is improved.

The invention discloses an infrared and visible light image registration method based on an improved SIFT algorithm, and the method comprises the steps: constructing a Gaussian difference pyramid of infrared and visible light images, so as to effectively capture the multi-scale features in the images; performing local adaptive FAST key point extraction on each layer of the pyramid, and detecting significant feature points of the image on a multi-scale level; based on gradient direction distribution of images in key point neighborhoods, main direction distribution is carried out on key points, so that the key points can still keep consistent under different rotation angles; coding the key points based on the gradient information features and generating corresponding feature descriptors to be applied to subsequent registration operation; and performing initial registration by using the similarity between the descriptors, verifying and optimizing an initial registration result by using an optimized RANSAC algorithm, and eliminating mismatching points. According to the method, the number of infrared and visible light image feature point detection and matching is increased, the time complexity is reduced, and the matching precision is ensured.

The invention discloses a steel-wood combined beam-column joint damage identification method and system applying machine vision, and the method comprises the steps: S1, obtaining a visible light, near-infrared and thermal imaging multi-mode image sequence of a steel-wood combined beam-column joint, and carrying out the denoising processing through adaptive anisotropic diffusion filtering, and obtaining a denoised multi-mode image sequence; s2, performing Gaussian pyramid decomposition and Laplacian pyramid feature fusion on the de-noised multi-modal image sequence tensor to obtain a fusion feature map; s3, performing adaptive threshold segmentation and morphological processing on the fused feature map to obtain a final binary image; s4, training the deep learning network to obtain a trained deep learning network; and S5, performing damage identification by using the trained deep learning network. According to the method, the steel-wood combined beam column joint damage can be accurately identified, the segmentation probability graph is obtained, the segmentation result is marked, and the visualization of the damage area is realized.

The invention relates to the field of computer vision, in particular to an image feature recognition method based on computer vision, which comprises the following steps of: preprocessing an input image, removing image noise and correcting image gray deviation to obtain a preprocessed image; performing multi-scale feature extraction on the preprocessed image, obtaining image local texture features and global contour features under different scales, performing dynamic weight fusion, calculating dynamic fusion weight based on a feature response value and a local complexity factor, and performing dimension reduction processing on fusion features through a principal component analysis algorithm to obtain a target feature set; and matching the reference features, calculating the Euclidean distance between the target feature set and the reference features, determining an adaptive matching threshold, judging a matching result, and completing image feature recognition. According to the method, through adaptive filtering and gray level equalization preprocessing, Gaussian pyramid multi-scale feature extraction and dynamic fusion and adaptive threshold matching, feature extraction accuracy, characterization capability and recognition robustness are improved.

The invention discloses a color printed matter defect automatic detection system and method based on machine vision, and relates to the technical field of computer vision, and the method comprises the steps: converting a color image into a gray level image, generating a boundary enhanced gray level image through a Canny edge detection algorithm, calculating the offset of edge pixels through sub-pixel recovery, and obtaining a color printed matter defect detection result. Extracting an edge pixel proportion based on the sub-region division table, performing amplification tracking on edge dense sub-regions, and analyzing RGB color deviation; through a multi-scale Gaussian pyramid decomposition and edge refinement technology, analyzing multi-scale features of the boundary enhanced grayscale image and optimizing edge precision; and classifying edge defects and color defects of each sub-region by utilizing a defect threshold value, calculating a comprehensive risk value, increasing a risk level according to a boundary condition, and generating a detection report. According to the method, the Canny edge detection algorithm is combined with sub-pixel recovery, so that the edge extraction precision and the positioning capability of color printed matter defect detection are improved.

The invention discloses a seamless image restoration method based on regional parallel adaptive pixel consistency generation, and the method comprises the steps: receiving an original image and a shielding mask image, obtaining the features of a multi-scale image through Gaussian pyramid decomposition and an edge detector, and achieving the adaptive region division and priority sorting through a watershed algorithm; extracting structural features by using a feature pyramid network and a non-local self-attention mechanism, and establishing a regional association graph through multi-scale attention calculation; generating a scheduling table based on the region priority and the association relationship, and performing parallel region generation; and calculating pixel continuity and structural consistency between adjacent regions, realizing multi-scale fusion, and generating a final restored image. The visual continuity and the structural integrity of the repairing result can be effectively guaranteed, and meanwhile the processing efficiency is improved.

The invention discloses a mechanical arm anti-interference control method and system based on visual servo, and relates to the technical field of mechanical arm anti-interference control, and the method comprises the steps: collecting an initial image through an industrial camera during the operation of a mechanical arm, and carrying out the preprocessing; selecting a pixel point from the central area of the initial image as a starting point by using a random walk algorithm, generating a step length in a random direction, calculating the next coordinate of the starting point, cutting, obtaining a walk path set, carrying out pixel value disturbance, generating a pseudo-abnormal image, and carrying out two-dimensional position coding based on the pseudo-abnormal image to obtain an embedded sequence vector; based on the embedded sequence vector, using a repair model to obtain a repair image, and generating a reconstructed image in combination with a deconvolution operation; by introducing the random walk algorithm, the multi-head self-attention mechanism, the Gaussian pyramid and the sparse dictionary, the perceptual robustness, the task execution stability and the generalization ability of the mechanical arm in the disturbed environment are remarkably improved.

The invention relates to an image noise reduction method and system and a readable storage medium, and the method comprises the steps: carrying out the Gaussian filtering of a current frame image, carrying out the down-sampling, obtaining a first down-sampling image, and carrying out the Gaussian pyramid decomposition, and obtaining a current frame image group; performing up-sampling to obtain an up-sampling image, and subtracting the up-sampling image from the current frame image to obtain high-frequency information; motion estimation and texture estimation under different scales are carried out on the current frame image group and the reference frame image group, and guided filtering correction is carried out; performing time-space domain filtering on the first down-sampling image and the reference frame image based on the motion estimation weight; performing up-sampling on the down-sampling image after noise reduction to obtain an up-sampling image after noise reduction, and performing weighted fusion on the up-sampling image after noise reduction and the high-frequency information according to the texture estimation weight to obtain a final image; and performing down-sampling on the final image and performing Gaussian pyramid decomposition to obtain a reference frame image group for noise reduction of the next frame image. According to the method, more texture details can be reserved while the signal-to-noise ratio of the image is increased.

The invention discloses a deep learning-based mixed-shooting image correction method and system, and relates to the field of computer vision, and the method comprises the steps: carrying out the illumination normalization processing of a mixed-shooting image group, and constructing a Gaussian pyramid image group; inputting the Gaussian pyramid image group into a ResNet-34 network to extract a multi-scale feature map, and generating spatial position and channel statistical information; and converting the spatial position and the channel statistical information into a DNA sequence fragment through a DNA base mapping rule. Illumination normalization is achieved through Retinex decomposition and Gaussian filtering, the exposure difference of different devices is effectively eliminated, the input quality of follow-up processing is guaranteed, traditional feature description is converted into biological sequence comparison through DNA base coding, geometric parameters are optimized in combination with quantum annealing, and the accuracy of the method is improved. The matching precision of a low-texture area and the correction effect of a large-view-angle-difference scene are remarkably improved, and the sub-pixel-level matching precision is achieved based on weighted RANSAC and multi-scale pyramid fusion.

The invention discloses an optical flow positioning method of an unmanned aerial vehicle and the unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle positioning, and the method comprises the steps: collecting a continuous image sequence in the flight process of the unmanned aerial vehicle in real time, and constructing a Gaussian pyramid for the collected images; and then illumination compensation is carried out between adjacent frames, speed information of the unmanned aerial vehicle is calculated, feedback compensation is carried out on the speed information of the unmanned aerial vehicle, and accurate positioning of the unmanned aerial vehicle is realized. By inserting the progressive frame between the adjacent frames with the overlarge gray difference value, gradual brightness transition can be realized between the adjacent frames with the overlarge gray difference value, the appearance change of a target can be smoothed, a tracker is helped to adapt to illumination change, visual and algorithm difficulties caused by sudden brightness change are reduced, and the tracking accuracy is improved. And meanwhile, the progressive frame is inserted between the adjacent frames with overlarge gray difference values to provide more references for feature point matching, so that the wrong matching rate of the feature points is reduced, and the accuracy and robustness of unmanned aerial vehicle positioning are improved.

The invention relates to the technical field of computer vision and robot positioning, and discloses a dynamic 3DGS-SLAM method and system based on Gaussian pyramid and adaptive densification. The method comprises the following steps: generating a semantic mask through real-time dynamic target detection so as to distinguish a static background from a dynamic object; constructing a 3D Gaussian map model of the scene; rendering images in parallel on a full-resolution view and a down-sampling resolution view by adopting a motion perception Gaussian pyramid rendering method, and fusing rendering results by utilizing a dynamic mask so as to eliminate motion edge artifacts; a self-adaptive densification strategy based on a monotonic attenuation function is adopted, the initialization radius of the Gaussian ellipsoid is dynamically adjusted based on the training progress, and high-density initialization and progressive redundancy pruning are executed to optimize the geometric fidelity; and finally, performing coarse-to-fine camera pose optimization, and outputting a precise pose and a high-fidelity static map. According to the method, the problems of tracking drift and rendering artifacts under dynamic interference are effectively solved, and the positioning precision and the rendering quality are improved.

The invention discloses an equipment micro-vibration real-time monitoring method and system based on visual enhancement, and belongs to the field of equipment micro-vibration monitoring, and the method comprises the following steps: S1, collecting a high-frame-rate video, and recording collection parameters in real time; s2, constructing a Gaussian pyramid, constructing a Laplacian pyramid through adjacent layer image difference of the Gaussian pyramid, and decomposing the collected video into different spatial frequency bands; s3, designing a band-pass filter to extract a specific frequency band where the equipment vibration signal is located, and enhancing and reconstructing the micro vibration signal; s4, carrying out region selection from the enhanced image frame, calculating an optical flow in a selected region through an optical flow method, and extracting a characteristic frequency of vibration; and S5, performing data visualization, and generating a vibration signal image in real time to display the motion characteristics of the target area. According to the invention, the problems of insufficient sensitivity and poor real-time performance of small vibration detection of equipment in a traditional vibration monitoring method can be solved.

The invention discloses an image-based train in-station operation state monitoring and identification method and system, and relates to the technical field of train state identification, and the method comprises the steps: collecting image data, carrying out the two-dimensional wavelet decomposition, carrying out the denoising processing through a soft threshold value, and regenerating the denoised alignment image data; gaussian pyramids for aligning the image data are constructed, equalization mapping is carried out through histogram equalization, and complete image data are combined based on each layer of Gaussian pyramids. According to the method, local details of the image are remarkably improved under different illumination conditions through CLAHE self-adaptive contrast adjustment, so that an original region with relatively low contrast becomes clearer, image levels with different resolutions are generated through multi-scale feature extraction and Gaussian pyramid decomposition, and the image resolution is improved. The structure information of the image under different scales is ensured to be reserved, and through the combination of the SGM and the RANSAC, the point cloud still has a strong anti-noise capability in a complex scene, and can adapt to environmental conditions such as illumination change and reflection interference.

The invention relates to the technical field of image data processing, in particular to a tourism management-oriented scenic spot three-dimensional modeling method and system, and the method comprises the steps: obtaining a scenic spot image of a target scenic spot, and determining the character possibility information and Gaussian pyramid in the scenic spot image; correcting a difference image between adjacent layers of the Gaussian pyramid by utilizing the character possibility information to obtain a target Gaussian difference pyramid; and based on the target Gaussian difference pyramid, determining multi-view three-dimensional modeling of the target scenic area. According to the method, the Gaussian difference pyramid for removing the influence of the passengers is constructed, so that the feature points selected by SIFT only comprise key points of the scenic spot as far as possible and do not comprise key points causing interference of the passengers, the precision and reliability of three-dimensional modeling of the scenic spot are improved, and the situations of deformity, missing, distortion and the like during three-dimensional reconstruction are avoided.

The invention discloses a filtering adjustment method and system based on an adaptive clock, and relates to the technical field of filtering adjustment, and the method comprises the steps: carrying out the time domain framing processing of a collected input signal through employing a sliding window, and combining DCT and compressed sensing to reconstruct the signal, and the main frequency of the signal and the dynamic change of the main frequency in the time sequence are accurately extracted through FFT, Gaussian pyramid layering and a multi-scale optical flow method, so that the frequency identification capability of the non-stationary signal is effectively enhanced, the frequency spectrum resolution is ensured, the data processing amount is remarkably reduced, the response speed of the signal frequency change is improved, and the method is suitable for popularization and application. Through the main-sub clock structure, the quick response of the center frequency of the filter to the change of the main frequency is ensured, the selectivity of the filter and the signal fidelity are improved, through the compensation adjustment of environmental factors, the influence of temperature and voltage changes on signal processing can be effectively reduced, and the robustness and stability of the system are enhanced.

The invention relates to the technical field of deburring, in particular to deburring equipment of a tool jig and an automatic burr positioning method. The method comprises the following steps: acquiring a plurality of monitoring images of a to-be-deburred tool jig under each view angle; performing Gaussian pyramid scale transformation on each monitoring image, performing angular point detection, and dividing the angular points in each monitoring image into two categories according to the presentation condition of the angular points in a scale image; obtaining a texture expression value of each angular point in the scale image by combining the relative distance between each angular point and other angular points in the corresponding scale image of each monitoring image and the edge change condition; and obtaining a texture feature value of each angular point in the monitoring image by combining the texture representation value of each angular point in the corresponding scale image in all the monitoring images under the same visual angle, and further dividing and screening a burr region for each category. According to the invention, the positioning accuracy of the burr area and the burr removal effect are improved.

The invention discloses a target detection method and device based on artificial intelligence, and relates to the technical field of artificial intelligence detection, and the method comprises the steps: collecting image data, determining the optical characteristics of the image data, and carrying out the quality screening according to the contrast; pixel enhancement is carried out based on the optical characteristics of the image data, Gaussian pyramid decomposition is carried out, and image components of different scales are generated; a feature map of a corresponding scale is determined based on the image components. According to the method, the distinction degree of the target and the background is improved through the characteristic difference between the brightness component and the background light, the details of the image are enhanced by combining the background light and the brightness component, the image quality in a complex environment is effectively improved, and the image quality is improved through the combination of the Gaussian pyramid and the wavelet decomposition. The multi-scale and multi-direction features of the target are effectively extracted, the target distinguishing capability in a complex scene is enhanced, and the balance of high-frequency and low-frequency features is ensured by a dynamic weight calculation mechanism.

The invention discloses a self-adaptive crack identification and quantification method for the periphery of an open stope. The method comprises an image preprocessing step and a crack identification step. According to the method, aiming at the complexity of an open stope environment, a Gaussian pyramid is adopted to carry out multi-scale analysis on an image, and gray scale equalization is realized through gamma transformation so as to adapt to crack identification requirements under different illumination conditions; according to the crack recognition part, an improved difference entropy is used for optimizing a local threshold segmentation algorithm, high-efficiency and high-precision detection of the stope crack is achieved, the segmentation threshold can be dynamically determined according to gray level distribution of an image local area, and the accuracy and robustness of crack recognition are effectively improved; according to the method, the automation level of crack detection is greatly improved, powerful technical support is provided for safety management and risk early warning of the open stope, and the method has important practical application value and wide market prospects.

The invention provides a depression image detection method fusing a Gaussian pyramid and a spatial domain. The method comprises the following steps: acquiring an original face image to be processed; the trained detection model is used for processing the original face image to obtain a detection result, and the detection model comprises a primary feature extraction module, a fusion module, a Gaussian pyramid attention module, a spatial domain attention module and a post-processing module. A Gaussian pyramid attention module is constructed and used for capturing the dynamics and diversity of multi-scale features, so that the perception ability of the model to different-scale information is enhanced; further mining significant features in the image by introducing a spatial domain attention module and based on a self-adaptive perception mechanism of a key region; and finally, through deep fusion of a Gaussian pyramid attention module and a spatial domain attention module, the integrity and discrimination of feature expression are improved, so that the detection precision of the detection model is effectively improved, and the model is low in calculation complexity and high in applicability.

The invention discloses a robot visual servo control system and control method based on dense tracking, and the method comprises the steps: carrying out the environment perception of a target robot, obtaining environment perception information, carrying out the image preprocessing of the environment perception, and obtaining the preprocessed environment perception information; a three-level Gaussian pyramid is constructed, multi-scale optical flow field construction is carried out according to the preprocessed environment perception information, and a bidirectional optical flow verification mechanism is set to carry out mismatching elimination; extracting a plurality of feature points of the current frame of image according to the constructed multi-scale optical field, introducing a DBSCAN spatial clustering algorithm to perform active point extraction to obtain a dynamic active point set, analyzing whether an error exists between a real-time operation track of the target robot at the current moment and a preset path based on the dynamic active point set, and if an error exists, determining that the target robot is in a real-time state. And if so, constructing a servo control scheme to carry out real-time error compensation. Therefore, high-precision and high-robustness visual servo control is realized.

The invention belongs to the technical field of image enhancement, and particularly discloses a low-illumination image enhancement method based on an information fusion strategy, and the method comprises the steps: carrying out the processing of an inputted low-illumination image through an image enhancement algorithm based on adaptive Gamma correction, and generating a first group of multi-exposure image sequences; processing the same input image through an improved quadratic function enhancement algorithm to generate a second group of multi-exposure image sequences; and performing multi-scale fusion on the first group of multi-exposure image sequences and the second group of multi-exposure image sequences, including distributing the weight of each image through a weight strategy, performing multi-scale decomposition on the images by using a Gaussian pyramid and a Laplacian pyramid, and reconstructing and generating a final enhanced image after layer-by-layer fusion. According to the method provided by the invention, the advantages of brightness enhancement and color optimization are combined, and an image with balanced brightness, rich details and natural colors can be generated in a low-illumination scene.

Method for detecting surface defects of metal strip based on CFLOW-AD model, obtaining surface images of defect-free metal strip, constructing a training data set after amplification, building a PatchSVDD feature extraction model, importing the training data set for feature extraction pre-training to obtain a pre-trained model; passing the pre-trained model into the encoding layer of the CFLOW-AD model as a feature extractor; extracting features of the Gaussian pyramid of normal metal strip to construct a multi-scale feature pyramid; training independent decoders for each scale layer using the training data set; using the trained CFLOW-AD model to detect defective test sample images to determine the area of the defect on the image. Self-supervised learning is carried out using defect-free samples, solving the problem of difficult collection of defect samples, and solving image noise caused by imaging factors through data amplification, improving robustness and reducing noise misdetection.

The invention relates to a health assessment technology, in particular to a tongue picture feature extraction and health assessment method based on deep learning, which is used for solving the problems of difficulty in normal judgment of a tongue picture, incapability of accurately tracking a tongue body motion trail and incapability of acquiring detailed parameters caused by incomplete capture of traditional static tongue picture information, oral cleanliness, illumination influence and unstable support. According to the invention, illumination and support are preset by setting the preset standard of the mouthwash, so that the problem of inaccurate tongue picture data caused by incomplete oral cleaning and unstable collection environment in the collection process is effectively solved; a Gaussian pyramid and a DoG pyramid are constructed, local extreme points are detected to preprocess a dynamic video, the image stability and the feature point accuracy are improved, through comparison with a demonstration video, quantitative analysis is carried out on tongue body motion vectors in the aspects of the number, the direction, the tremor amplitude, the offset direction and the like, and health factors and disease possibilities are calculated.

The invention belongs to laser stripe image data processing, and particularly relates to a multi-scale gradient gravity center laser center line extraction method and system, and the method comprises the steps: S1, carrying out the feature extraction and enhancement of a laser stripe image, intensifying detail branches through a space channel weight map, obtaining a bounding box positioning result based on depth separable convolution and residual connection, and obtaining a bounding box positioning result; s2, acquiring a prediction center for each column by adopting a prediction method, and searching a gray peak value in a normal adaptive window to realize coarse positioning; the method comprises the following steps: S1, building a local coordinate system and an elliptical window along the coarse center line, and constructing a multi-scale Gaussian pyramid to carry out background suppression and image enhancement, S4, generating a weighted graph through cross-scale fusion, calculating a weighted gray gravity center in each window, and carrying out image enhancement on the weighted graph. And after smoothing, a high-precision laser stripe center line is output. And the accuracy and robustness of extracting the laser center line are improved.

The invention relates to a monocular neural rendering SLAM method and system in a complex environment. According to the method, an initial image sequence in a complex environment is obtained and preprocessed, then a pre-trained semantic segmentation model is input, and a multi-dynamic object mask is obtained; extracting point features and line features from the preprocessed image sequence, and eliminating corresponding dynamic features by using a multi-dynamic object mask to obtain static features; performing feature matching on the static features, solving a conversion matrix, and obtaining an estimated pose by optimizing a point-line combined re-projection error function; obtaining sparse superprimitives, recognizing a to-be-densified area according to the re-projection error of the points, and generating a new superprimitive set in combination with unmatched feature points; and obtaining a rendered image, and performing multi-scale Gaussian pyramid supervised training on the rendered image to obtain a final global map. Compared with the prior art, the method has the advantages of improving the pose estimation accuracy and map characterization capability of the SLAM in a complex environment and the like.

The invention discloses an underwater image enhancement method and system based on three-input multi-scale fusion. The method comprises the following steps: carrying out visibility recovery on an image to be processed; performing contrast enhancement through a nonlinear mapping function; carrying out enhancement processing on the contour of the high response area; respectively calculating a Laplacian contrast weight map, a saliency weight map and a saturation weight map of the three processed images; linearly combining all types of weight maps to obtain an aggregation weight map, and normalizing the aggregation weight to obtain a normalized weight map; decomposing the three processed images into a Laplacian pyramid, decomposing the normalized weight map into a Gaussian pyramid, and performing fusion through upward sampling and addition reconstruction; carrying out smoothing processing and edge-preserving sharpening processing on the fused image to obtain an image after underwater image enhancement and restoration; according to the method, the color cast problem of the underwater image is solved, the image contrast is effectively improved, and the texture details of the image are improved.