8results about How to "Good details" patented technology

The invention belongs to the technical field of stereoscopic vision, and discloses a stereoscopic matching method based on unsupervised online learning. The method is realized through a stereo matching module based on a multi-level cost body, a non-occlusion region confidence estimation module and a Mann-Kendall trend detection module. Compared with the prior art, the method has a better stereo matching effect. The model provided by the invention has the best result in both the initial stage and the later stage, and due to the good generalization of the model, the model can always keep a relatively accurate prediction result in a sheltered region. The model has a good prediction result in the occlusion area due to high generalization, and the tendency of parameter error update is relieved due to the fact that the self-adaptive adjustment strategy provided by the invention eliminates the influence of error loss of most of the occlusion areas, so that the network can maintain the optimal performance all the time.

This utility model discloses a 3D scanner with replaceable filters, comprising: an instrument body; a camera unit and a laser projection unit disposed within the instrument body, wherein the camera in the camera unit and the projection surface in the laser projection unit are both disposed on the measurement surface of the instrument body; and a supplementary lighting unit located around the camera and disposed on the measurement surface, wherein the filter unit in the supplementary lighting unit is detachably disposed at the front end of the light source assembly in the supplementary lighting unit. This utility model significantly improves the scanner's adaptability to different material surfaces and scanning quality through the design of replaceable filter units, optimizes image clarity and detail, and allows for flexible adjustment and rapid replacement of filters according to the characteristics of different materials.

The application discloses a low-contrast image joint enhancement and super-resolution reconstruction method, which comprises the following steps: firstly, performing multi-scale Gaussian filtering on the low-contrast image; secondly, expanding the imaging dynamic range of the quotient image by using an S function; thirdly, performing color correction on the expansion result to obtain a contrast-improved image; and finally, optimizing the resolution and detail information of the contrast-improved image by a blind super-resolution reconstruction model constrained by a total variation to obtain a detail information and resolution improvement result. The low-contrast image joint enhancement and super-resolution reconstruction can help to overcome the influence of image adverse factors, such as low contrast / resolution and fuzzy details, and lay a foundation for image feature extraction and image interpretation.

The present invention discloses a method for clearing visible light OCT images based on a generative adversarial network. The method is as follows: 1. Acquisition of training samples. Use a visible light OCT device to obtain a V-domain image. Use a near-infrared light OCT device to obtain an N-domain image. 2. Preprocess the V-domain image and the N-domain image obtained in step 1. 3. Establish a training sample data set based on the preprocessed V-domain image and the N-domain image. 4. Construct and train an OCT image clearing model. 5. Input the visible light OCT image into the generator G1 to obtain an OCT image with improved quality. The new unsupervised generative adversarial network proposed in the present invention constructs an OCT image clearing model with the help of the idea of ​​dual learning, and can obtain better results without using paired images for training. In addition, the present invention adds a network that can edit image attributes, which enhances the ability of the generator to learn clear structures during dual training.

The invention discloses a multi-focus microscopic image fusion method based on a convolutional neural network. The convolutional neural network is trained to fuse microscopic images of a plurality of focal layers. A plurality of partially focused images in the same scene are given, and feature extraction is performed through a convolutional neural network to obtain significant image features. Afterwards, the convolution features of a plurality of input images are fused according to the element-by-element maximum fusion rule, and the fused features are reconstructed through two convolution layers, so that a fused image with rich information is generated. According to the method, the images of multiple focal planes of the pathological section under microscopic scanning are input and effectively fused, so that the detail representation of the microscopic image is improved, the fused image has good visibility in focal and non-focal areas, and the advantages of flexibility and generalization of deep learning are fully utilized. Parameters which need to be manually and continuously adjusted in a traditional method are replaced by a neural network structure, and convergence of an algorithm and reconstruction precision are theoretically supported.

The application provides an image super-resolution processing method and device, electronic equipment and storage medium. The method comprises the following steps: receiving a user input image to be processed; inputting the image to be processed into a lightweight neural network model, extracting detailed features of the image to be processed by the lightweight neural network, and fusing the extracted detailed feature map and the image to be processed to obtain a super-resolution image corresponding to the image to be processed. The user can directly process image data online on the terminal device, and the lightweight neural network composed of a convolution layer, a plurality of structure blocks and an up-sampling layer can greatly improve the image processing speed, avoid time-consuming network transmission and achieve real-time image processing.

The invention discloses a three-dimensional reconstruction method based on a single image, including a training phase: the training samples include sampling points on a three-dimensional model and actual implicit function values ​​corresponding to the sampling points, and a single image corresponding to the three-dimensional model; a feature space is generated through a single input image, the sampling points of the three-dimensional model are used as query points, the implicit function values ​​of the query points are predicted, and the loss is calculated according to the predicted implicit function values ​​and the actual implicit function values ​​to complete the fitting of the implicit function field; a reconstruction phase: after the training phase is completed, a feature space is generated for the single input image, a number of points are sampled in the observation space, and these points constitute the query points; the implicit function values ​​of the query points are predicted; the query points and their predicted implicit function values ​​are input into a marching cubes algorithm to reconstruct the three-dimensional model. The method improves the problem of serious lack of prior conditions in single image reconstruction and can obtain fine details in the visible part.

The present application relates to the field of 3D modeling, and specifically discloses a method and system for constructing a 3D model of a smart city, which uses a data processing technology based on deep learning to extract features from the 3D point cloud data of the urban area to be modeled, captures the semantic features of the 3D structure of the urban area, and combines the natural language description of the characteristics of the urban area input by the user to perform feature modulation optimization processing on the 3D structure of the urban area, further optimizes the detailed performance of the 3D model, and thus realizes the intelligent construction of the 3D model of the urban area. In this way, the geometric information of the urban area and the language description information of the urban characteristics can be effectively integrated, so that the generated 3D model of the urban area can more accurately reflect the internal structure and functional layout of the city, and improve the application value of the model in urban planning and management.