122 results about "Laplacian pyramid" patented technology

The invention discloses an intelligent steel pipe surface defect recognition system based on deep learning, and particularly relates to the technical field of pipe surface defect analysis. An annular polarization light source array and a high-frame-rate CMOS sensor are adopted to synchronously collect visible light and near-infrared multi-polarization images; a surface normal is calculated based on Stokes parameters, mirror surface suppression and diffuse reflection enhancement are realized, a defect candidate area is generated by fusing multi-scale Laplacian pyramid residual error and Renyi entropy segmentation threshold positioning, multi-physical quantity registration is completed through white light interference and infrared thermal imaging, a six-channel feature cube is constructed, and a three-dimensional image is obtained. According to the method, space, spectrum and thermal characteristics are jointly extracted in the multi-head attention convolutional neural network, the confidence coefficient is evaluated in combination with Jensen-Shannon divergence, and the polarization angle and the focal length are dynamically adjusted according to the confidence coefficient, so that closed-loop parameter self-optimization is realized, and the micro-scale pitting corrosion and millimeter-scale crack detection precision is remarkably improved.

The invention relates to an underwater image processing technology in the field of underwater autonomous perception, in particular to an underwater low-quality image enhancement method based on a Laplacian pyramid and contrast learning. Comprising the following steps: S1, image input and multi-scale decomposition: decomposing an input image into a low-frequency residual layer and a plurality of high-frequency detail layers through Laplacian pyramid decomposition; s2, inputting the low-frequency residual error layer into a global illumination and color correction sub-module to obtain an enhanced low-frequency residual error layer; s3, inputting the enhanced low-frequency residual error layer and the multi-scale high-frequency detail layer into a frequency domain enhancement feature module to obtain multi-scale enhanced high-frequency detail layer output; s4, performing progressive reconstruction on the enhanced low-frequency layer output and the high-frequency enhancement layer output of each scale according to the inverse process of the Laplacian pyramid; and S5, outputting a result and applying. The method can be used for sharpening optical image data in the operation task of the autonomous underwater vehicle, and the image quality is improved.

The invention relates to the technical field of underwater sediment measurement, in particular to a microspur measurement device and method, and provides the following scheme: firstly, screening out an initial local selected area through overall image graying and feature extraction, and adjusting the local area through iteration to ensure that a microspur image can accurately reflect particle features in the overall image; then, decomposing and extracting high-frequency edge information by using a Laplacian pyramid, eliminating background noise through gray variance, and generating a particle region graph; further separating adjacent particles by using an adhesive particle cutting technology, and finally generating a high-quality sediment image through image correction; according to the method, the microspur image acquisition frequency can be reduced, the particle measurement accuracy is improved, the data processing complexity is reduced, and the method has relatively high measurement precision and practical value.

The invention provides a flue-cured tobacco leaf image distortion correction enhancement method, and belongs to the technical field of flue-cured tobaccos. Distortion segmentation point detection is carried out on an obtained flue-cured tobacco leaf image, and an illumination change area caused by unevenness of a tobacco leaf surface is identified by using an illumination homogenization algorithm based on a Poisson equation; secondly, constructing a distortion jump point matrix to mark gray abrupt change positions caused by shadows and light reflection between adjacent areas, then decomposing and reconstructing multi-scale image features by adopting a Laplacian pyramid image fusion algorithm, and eliminating optical interference generated by surface fluctuation through a designed fusion rule; and finishing processing when the accuracy reaches a set range, otherwise, carrying out correction and optimization again, thereby solving the technical problem that the image quality and the subsequent analysis accuracy are influenced by shadow and light reflection generated during image acquisition due to unevenness of the tobacco leaves in the prior art.

The invention discloses a low-light image enhancement method based on multilevel feature fusion. The low-light image enhancement method comprises the steps of acquiring a data set, dividing the data set, extracting features, constructing a synchronous multi-scale network, training the synchronous multi-scale network and testing the synchronous multi-scale network. According to the synchronous multi-scale low-light image enhancement method in combination with the Laplacian pyramid, the input image is processed in parallel by adopting a double-path structure: the preliminary enhancement image is obtained through the local-global convolutional neural network, and the detail and texture information of the image is enhanced based on the Laplacian pyramid decomposition network. A multi-scale network is adopted to process images in scenes of deblurring, defogging, rain removal, low light enhancement and the like, and details and features are extracted in a layered manner, so that the definition, color and contrast ratio of the images are effectively improved. Comparison experiments prove that the method has the advantages that noise is effectively suppressed, and remarkable effects are achieved in the aspects of detail recovery and color restoration. The method is suitable for image enhancement processing under various complex illumination conditions.

The invention discloses a medical image segmentation method based on a Laplacian pyramid and a dynamic Transform. The method comprises the following steps: S1, forming a preprocessed medical image; s2, decomposing the preprocessed medical image into a plurality of image sub-layers with different scales; s3, generating a corresponding multi-scale feature representation; s4, inputting the multi-scale feature representation into a dynamic Transform module, and generating a fused global feature representation; and S5, decoding the fused global feature representation to generate a preliminary segmentation image, applying a post-processing algorithm to the preliminary segmentation image, and executing morphological operation, edge repair and noise removal to obtain a medical image segmentation result with clear boundary and regional coherence. According to the method, a medical image segmentation result with clear boundary, coherent region and accurate semantics is generated, the practicability of the model in clinical application is structurally improved, and the method has significant advantages in processing images with fuzzy tumor boundary and dense and overlapped tissues.

The invention relates to the technical field of image segmentation, in particular to an anaphora image segmentation method based on space-frequency dual tuning, and the method comprises the steps: dynamically generating a frequency domain learning prompt and a space domain learning prompt through a space frequency domain dual prompt technology; splicing interaction is carried out by utilizing spatial domain prompts and encoder features, and semantic representation of an anaphora target is enhanced; recovering high-frequency details by adopting a Laplacian pyramid and a LoRA-based frequency domain encoder, and enhancing key features through multiplication operation prompted by a frequency domain; and finally, double-domain optimization features are fused, and an accurate boundary segmentation result is generated through a mask decoder, so that the problems of detail degradation and boundary fuzziness caused by lack of frequency domain information in a traditional method are effectively solved.

The invention discloses a multi-scene statistical channel fingerprint construction method and system based on frequency perception. According to the method, unified tensor modeling is carried out on statistical channel state information, and various typical statistical channel fingerprint construction scenes are uniformly expressed as a tensor recovery problem. On the basis, multi-scale frequency decomposition and reconstruction of the statistical channel fingerprints are realized through a Laplacian pyramid, and multi-scale frequency sub-band features of the statistical channel fingerprints are obtained while the reasoning efficiency is improved; and through a shared mask learning mechanism, carrying out layer-by-layer self-adaptive refinement processing on high-frequency statistical channel fingerprint components under different scales. Besides, in order to expand the characteristic receptive field under the condition that the model parameter quantity is not obviously increased, convolution processing is carried out on different frequency components in a wavelet transform domain, and decoupling extraction and efficient modeling of the statistical channel fingerprint multi-frequency characteristics are achieved. According to the method, the construction precision and the calculation efficiency can be considered in various statistical channel fingerprint construction scenes.

The invention belongs to the technical field of image analysis and processing, and relates to an automobile circuit board solder paste printing quality detection method and system. The method comprises the steps of constructing a Laplacian pyramid for a target solder paste area image, extracting multi-level geometric and position features, performing weighting based on local phase consistency to obtain a first fusion feature, and comparing the first fusion feature with a first fusion feature of a historical qualified sample to obtain a second fusion feature; calculating a time sequence drift correction characteristic capable of correcting the drift in the production process; constructing a neighborhood feature map by combining the first fusion features of the target region and the spatial neighborhood of the target region, and extracting spatial morphological features by using an orthogonal Gabor filter; and performing vector splicing on the first fusion feature, the time sequence drift correction feature and the spatial form feature to generate a comprehensive combined feature, and inputting the comprehensive combined feature into a predetermined classification model to obtain a printing quality detection result. According to the invention, the detection rate of the solder paste area defect on the automobile circuit board can be improved.

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.

The invention discloses a panoramic image splicing method, system and device, and relates to the technical field of image processing, and the method comprises the steps: collecting images around a vehicle at different visual angles; using the MASK masks to generate overlapping region masks of the different view angle images, and performing local feature detection on the overlapping region masks to generate a transformation matrix; aligning the images of different visual angles according to the transformation matrix; a Canny edge detection operator is fused in splicing seam energy detection, the energy weight of the image edge area of the aligned image is enhanced, and a splicing seam is guided to actively avoid an object with remarkable structural features; b spline interpolation is introduced to optimize an initial splicing seam path; constructing a Laplacian pyramid along Gaussian blur of a splicing seam, and sequentially carrying out gradual fusion to generate a seamless panoramic image; according to the method, the basic function of eliminating the visual blind area is realized, a clear and visual panoramic view can be provided for a driver, and the driving safety is effectively improved.

The invention belongs to the technical field of image processing, and particularly relates to a physical prior guidance-based interpretable underwater image enhancement method, which comprises the following steps of: constructing a physical prior guidance-based diffusion model which comprises a high-frequency prior generation branch and a physical information perception diffusion model enhancement branch; in the high-frequency prior generation branch, firstly, an input underwater image is decomposed by using a Laplacian pyramid to obtain a multi-scale high-frequency feature map, the multi-scale high-frequency feature map is integrated with an original feature map extracted from the underwater image, and then a transmission map, a global background light map and a defogged clear underwater image are generated by combining an atmospheric scattering model; the diffusion model enhancement branch of physical information perception comprises a cross attention module and a diffusion model, and the diffusion model guides the diffusion process through the cross attention module by using a physical attribute graph to obtain an enhanced image. According to the method, the high-frequency information of the underwater image and the original image features are fused, and the internal relation between different scale features is enhanced, so that the semantic consistency of feature representation is improved.

The invention relates to the technical field of video generation, in particular to a video frame generation system and display equipment based on texture reconstruction and trajectory optimization, and the system comprises a motion breakpoint detection module, a trajectory interpolation frame generation module, a texture anomaly positioning module, a texture interpolation reconstruction module and a video frame synthesis module. According to the method, an edge feature map is generated through edge detection and non-maximum suppression, the boundary recognition precision is improved, noise is suppressed, candidate regions are divided through region growth, the local feature extraction efficiency is enhanced, global redundancy is reduced, three-frame optical flow analysis captures trajectory breakpoints, and motion vector correction path deviation is predicted in combination with a Lucas-Kanade method. Color consistency verification optimizes interpolation to reduce cross-frame abrupt change; 64 * 64 grids and a gray level co-occurrence matrix locate texture abnormity; five-frame similarity strengthens time sequence stability; Gabor filtering extracts a main direction and combines an adjacent frame reconstruction texture direction to ensure consistency; and trajectory smoothness and visual consistency are optimized through multi-frame cooperation and layered detection.

The invention belongs to the technical field of image processing, and discloses an underwater dredging robot terrain recognition method and system, and the method comprises the steps: obtaining an illumination attenuation coefficient through the shooting depth and plankton information, accurately correcting the influence of illumination on an image, and carrying out the brightness adjustment of the image based on the illumination attenuation coefficient, so as to enable the terrain features to be more prominent. Utilizing Laplace decomposition to obtain a multi-layer topographic image, and realizing multi-scale analysis; the accuracy of terrain analysis is improved by screening an edge texture region; the defogging weight is determined in combination with the illumination attenuation coefficient, and the dark channel prior is adopted for defogging recovery, so that the defogging effect is effectively removed, and the image visibility is improved. And reconstructing and synthesizing a complete image through an inverse Laplacian pyramid to obtain an underwater topographic image of which the quality is comprehensively improved, and further obtaining underwater topographic information. The method not only improves the image quality, but also achieves the precise analysis and recognition of the underwater topography, and provides powerful technical support for the research of the underwater topography.

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.

The invention relates to the technical field of digital image processing, in particular to a real-time image quality enhancement method for a vehicle-mounted camera EMC test, and the method comprises the steps: obtaining real-time image data and historical image data; and performing multi-branch collaborative decoupling processing on the real-time image data to generate a frequency domain purification graph, a core structure graph, a space domain purification graph and a time domain stability graph. And performing local statistical analysis on the real-time image data to generate an artifact intensity map and a perception saliency map. Under a multi-resolution Laplacian pyramid framework, fusing the frequency domain purification graph and the space domain purification graph; the weight of the fusion process is intelligently regulated and controlled spatially and hierarchically by an artifact intensity graph, a perception saliency graph and a time domain stabilization graph which are constructed based on a Gaussian pyramid, and finally a high-quality enhanced image is reconstructed and generated. According to the invention, through a multi-branch cooperative decoupling and intelligent fusion method, real-time enhancement of the vehicle-mounted camera image under EMC interference is realized.

The invention discloses a self-adaptive fatigue state detection method based on multi-modal feature fusion, and belongs to the technical field of fatigue detection, and the method comprises the steps: synchronously collecting facial images through an infrared camera and an RGB camera, carrying out the image fusion through a Laplacian pyramid method, extracting facial features, setting a personalized threshold value, and obtaining fatigue features. Performing special scene judgment under limited face recognition, considering micro-expression features, performing fatigue parameter supplementation, judging a fatigue state through a comprehensive fatigue model with multi-fatigue feature fusion, and performing early warning information reminding; according to the adaptive fatigue state detection method based on multi-modal feature fusion, through multi-modal feature fusion, image details are effectively reserved, a low-light environment is adapted, facial feature deficiency is compensated, and the accuracy and stability of fatigue state detection are remarkably improved by combining personalized threshold values and special scene judgment.

The invention discloses a spacecraft micro-deformation measurement method and system based on femtosecond laser interference, and relates to the technical field of laser interference measurement, and the method comprises the steps: extracting an interference phase signal corresponding to residual distribution, obtaining frequency domain representation through fast short-time Fourier transform, and calculating the spectral intensity; performing frequency domain decomposition on the spectral intensity by using a Laplacian pyramid, calculating local energy density and performing normalization processing to obtain a scale weight, converting residual distribution into a frequency domain deformation spectrum through Fourier transform, and performing weighted fusion in combination with the scale weight to generate a fusion deformation field; the measurement precision and real-time performance in a complex environment are improved by accurately controlling the optical main axis, temperature compensation and interference fringe analysis, deformation field information can be accurately extracted by combining the Laplacian pyramid and the frequency domain fusion technology, and the measurement sensitivity and accuracy are improved.

The present invention discloses a low-light image enhancement method based on deep learning and Laplacian pyramid. The method comprises the following steps: decomposing the low-light image into a single LF component and N HF components using a Laplacian pyramid decomposition function; enhancing the LF component using a low-frequency enhancement module; fusing the pre-enhanced LF component, the enhanced LF component, and the Nth HF component, and inputting the fusion into a cross-layer feature enhancement module, where the LF component guides the enhancement of the Nth HF component, with i set to N; inputting the enhanced i-th HF component and the unenhanced i-1th HF component into the cross-layer feature enhancement module, where the i-th HF component guides the enhancement of the i-1th HF component; if i is not 1, then i is set to i-1 and the previous step is repeated; otherwise, all frequency components are enhanced, and a light-enhanced image is reconstructed using a Laplacian pyramid reconstruction function. The method achieves better performance than existing methods while maintaining a relatively small model size and low computational complexity.

The application discloses a large-format cross-scale adhesive film defect detection method and device. The method comprises the following steps: rough focusing of a camera and capturing an adhesive film image, rough scanning of the adhesive film image and extraction of information of printed graphics in the adhesive film image by using an image difference method, input of an output differential image into an anchor frame algorithm, input of output fine scanning target region coordinates into a genetic algorithm, combination of motor S-curve planning characteristics to output an optimized fine scanning path; driving a motion platform to complete path execution by using a double-shaft linkage control combined with the fine scanning path, focusing of the camera during path execution, collection of high-resolution images of defect regions and registration, image stitching and fusion of the registered images by using an optimal seam line algorithm combined with an improved Laplacian pyramid fusion method, and defect detection on the complete adhesive film image after fusion. The application has high stitching accuracy, fast processing efficiency, and can effectively retain more detailed information.

A polar unmanned vehicle image enhancement method and system based on multi-scale learning relates to the field of image exposure correction. This method addresses the problem that existing exposure correction and contrast enhancement methods can generally only correct underexposure errors but cannot improve image quality. The method includes: acquiring image data; constructing an image exposure correction architecture using an improved Laplacian pyramid, the image exposure correction architecture comprising: four subnets, the first subnet being a four-layer encoder-decoder network, the second and third subnets each being a three-layer encoder-decoder network; and the fourth subnet being a three-layer encoder-decoder network; and performing color correction and detail enhancement on the image data using the image exposure correction architecture. The present invention is applicable to the field of surface unmanned vehicle target recognition.

The present application belongs to the technical field of image processing, and particularly relates to a geological disaster risk assessment method and system based on big data, which comprises the following steps: collecting multi-level remote sensing videos with different time spans, performing STL decomposition on the pixel value sequence of the pixel points at the same position in the same layer of the Laplacian pyramid of each frame of the remote sensing videos, determining the magnification multiple according to the change of the obtained trend term and the level number of the corresponding remote sensing video, magnifying the periodic term of each group of pixel points, and then reconstructing the Laplacian pyramid to obtain the remote sensing videos after action magnification, determining the geological deformation probability within the corresponding time span according to the edge change in the remote sensing videos after action magnification, and thus obtaining the geological disaster probability. The present application magnifies the subtle geological changes in the remote sensing videos, and is more accurate in geological disaster risk assessment.

The present invention discloses a component-based three-dimensional model design method and system, which relates to the field of component-based three-dimensional model design and includes: building a three-dimensional model design platform based on three-dimensional design software; establishing a spatial rectangular coordinate system and dividing the three-dimensional scene into several cubes; establishing an effective surface optimization model to smooth the effective surfaces of the scene elements contained in each cube; further optimizing the multi-angle fusion of the three-dimensional scene components based on the Laplacian pyramid algorithm; establishing a color fusion optimization model to smoothly optimize the colors of the three-dimensional scene and the splicing interfaces of each component; and establishing a three-dimensional scene component fusion optimization model to obtain a three-dimensional scene model with high quality and high compatibility in component fusion. The advantages of the present invention are: effectively improving the quality and compatibility of the fusion between the three-dimensional scene and the components, and reducing the problems of incompatibility, gaps, overlaps or color breaks when the three-dimensional scene and the components are fused.

The application discloses a tone mapping method and system based on a lookup table and a Laplace filter, and belongs to the technical field of image processing.The input image is first decomposed into an adaptive Laplace pyramid;then a low-frequency image at the bottom layer of the adaptive Laplace pyramid is input into a weight predictor to obtain a pixel-level weight map, and a three-dimensional lookup table is used to perform three-linear interpolation on the low-frequency image to obtain a preliminary mapping image; then the preliminary mapping image and the weight map are fused pixel by pixel to generate an image at the bottom layer of the fine-tuned Laplace pyramid; then a filter parameter prediction module is used to learn a parameter value map of the remaining layer images of the adaptive Laplace pyramid, and the parameter value map is applied to a local Laplace filter to obtain the remaining layer images of the fine-tuned Laplace pyramid; finally, the fine-tuned Laplace pyramid is reconstructed to obtain a tone-mapped image.The application can retain local edge details of an image while performing global tone mapping.

A fast multimodal image fusion method, system, device, and medium based on a simplified adaptive parameter pulse-coupled neural network. The method comprises: converting the RGB channels of a color image into YUV channels, normalizing the Y channels of a grayscale image and a color image, decomposing the channels using a Laplace pyramid to obtain high-pass subbands and low-pass subbands, fusing the high-pass subbands using a SPAPCNN rule to obtain a high-frequency image, fusing the low-pass subbands using an MN-DWRE rule to obtain a low-frequency image, inversely decomposing the high-frequency and low-frequency images using an inverse Laplace pyramid to obtain the Y channel of the fused image, combining the Y channel with the U and V channels of the original image, converting the channel into an RGB channel, and performing normalization. The system, device, and medium are used to implement a fast multimodal image fusion method based on a simplified adaptive parameter pulse-coupled neural network. The present invention has the characteristics of fast decomposition speed, short fusion time, and good fusion effect.

The invention provides a grounding grid detection method and device based on image fusion, equipment and a storage medium. The method comprises the following steps: measuring a first detection image, a second detection image and a third detection image of a to-be-detected area; performing Laplacian pyramid fusion on the first detection image, the second detection image and the third detection image to obtain a grounding grid fusion image; performing edge detection on the grounding grid fusion image to obtain an edge image corresponding to the grounding grid fusion image; and detecting the edge strength of the corrosion area in the edge image to represent the corrosion degree of the corrosion area. According to the invention, three imaging technologies of electrical impedance, ultrasonic wave and infrared thermal are adopted, and through image registration and Laplacian pyramid fusion, non-destructive and real-time on-line detection of the state of the grounding grid is realized, multi-dimensional information such as resistivity, structure and temperature can be comprehensively reflected, the accuracy of corrosion positioning and evaluation is effectively improved, and the reliability of the system is improved. And visual and reliable technical support is provided for safe operation and maintenance of a power system.

The application discloses a medical image segmentation method based on Laplacian pyramid and dynamic Transformer, S1. Form a preprocessed medical image; S2. The preprocessed medical image is decomposed into a plurality of image sublayers of different scales; S3. Corresponding multi-scale feature representation is generated; S4. The multi-scale feature representation is input into the dynamic Transformer module to generate the fused global feature representation; S5. The fused global feature representation is subjected to decoding processing to generate a preliminary segmentation map, and a post-processing algorithm is applied to the preliminary segmentation map to perform morphological operation, edge repair and noise removal, and obtain a medical image segmentation result with clear boundaries and coherent regions. The application generates a medical image segmentation result with clear boundaries, coherent regions and accurate semantics, and improves the practicability of the model in clinical application, and has obvious advantages in processing images with blurred tumor boundaries and densely overlapped tissues.

This invention belongs to the field of medical image processing technology, specifically relating to an X-ray image enhancement method, system, and storage medium. The method includes: inputting an original X-ray image and reapplying window width and window level; performing noise reduction processing; copying the X-ray image into a normal tissue layer, a cancerous tissue layer, and a calcification point layer, adjusting the brightness and contrast of each layer using gamma adjustment; performing image enhancement and Retinex enhancement; acquiring the three enhanced layers and the Retinex-enhanced normal tissue layer, and performing Laplacian pyramid fusion to obtain the enhanced image. This invention also provides an X-ray image enhancement system for implementing the above method. This invention can achieve excellent image enhancement results without excessive magnification or contrast enhancement, supporting doctors in rapid and accurate diagnosis, and has excellent application prospects.

The invention relates to the technical field of three-dimensional reconstruction, and discloses a face three-dimensional reconstruction method, system and device based on a portable device and a medium, and the method comprises the steps: collecting a face image frame sequence of a target user through the portable device; performing face key point detection on each frame of face image in the face image frame sequence, and performing face geometric reconstruction based on a face key point result in combination with a multi-stage fusion algorithm to obtain a face geometric reconstruction model; determining a plurality of key face images in the face image frame sequence based on a frame selection strategy, performing de-illumination processing on each key face image, and fusing each de-illuminated face texture image through a Laplacian pyramid network to generate a face texture reconstruction model; and mapping the face texture reconstruction model to the face geometric reconstruction model to obtain a complete face three-dimensional reconstruction result. According to the invention, the hardware cost can be reduced, the convenience is improved, remote medical treatment is realized, and the face three-dimensional reconstruction precision and quality are improved.

This invention discloses a method and system for transferring the style of traditional Chinese painting based on the Laplacian pyramid, belonging to the field of computer vision technology. The method constructs a multi-scale style transfer model based on the Laplacian pyramid. The multi-scale style transfer model includes a Laplacian pyramid decomposition module, a basic style transfer network, a detail enhancement network, an edge information selection module, and a Laplacian pyramid reconstruction module. Preprocessed content image datasets and preprocessed style image datasets are input into the multi-scale style transfer model for training. Style loss functions and content loss functions are used to optimize the multi-scale style transfer model, resulting in a multi-scale style transfer generation model. The target content image is then input into the multi-scale style transfer generation model to generate a stylized image in the style of traditional Chinese painting. This invention can generate high-quality stylized images with the style of traditional Chinese painting.