38results about How to "Rich detail features" patented technology

The invention discloses an image processing method, an image processing device, a storage medium and a mobile terminal. The image processing method is applied to the mobile terminal and comprises thesteps of: acquiring an original image collected by the mobile terminal, wherein the color depth of the original image is at least 10 bits per pixel; determining a brightest region in the original image, and performing exposure processing on the brightest region to obtain intermediate image data; and performing logarithm conversion on the intermediate image data to obtain a target image. Accordingto the image processing method of the invention, gamma correction does not need to be carried out on the intermediate image data, but logarithm conversion is carried out, and more detail features of bright parts and dark parts of the image are reserved by using logarithm conversion; and on the other hand, exposure processing is carried out according to the brightest region, detail feature loss isavoided, and more detail features are reserved.

The invention discloses a three-dimensional face reconstruction method and device, electronic equipment and a storage medium, and relates to the technical field of electronic equipment. The method comprises the following steps: acquiring a shape parameter and a texture parameter of a to-be-reconstructed face, inputting the shape parameter into a first model to obtain a three-dimensional face shape output by the first model, and inputting the texture parameter into a second model to obtain a face texture map output by the second model, wherein, at least one of the first model and the second model is obtained based on generative adversarial network training, a target three-dimensional face is generated based on the three-dimensional face shape and the face texture map, and the target three-dimensional face comprises texture information generated based on the face texture map. According to the method, the three-dimensional face shape and / or the face texture map are / is generated through the trained generative adversarial network, so that the generated target three-dimensional face has rich detail features, and the reconstruction effect of the three-dimensional face is improved.

The invention discloses a method for optimizing HarDNet-Lite on an embedded platform, which is used for solving the problems that the existing target detection network is too complex, the operand is large, the reasoning speed on the embedded platform is low, and the positioning precision is low. The method comprises the following steps: 1) establishing a lightweight HarDNetLite feature extractionnetwork; 2) fusing feature maps of different scales by adopting a weighted FPN structure, so rich underlying detail information and high-level semantic information are fully fused; 3) generating a YOLO detection head, and placing an anchor frame generated through k-means clustering on the feature maps of different sizes to detect targets of different sizes; 4) performing end-to-end model trainingby using a classification and regression loss function; 5) deploying the trained model on an embedded platform, and carrying out target detection. The invention has the beneficial effects that the reasoning speed and the target detection precision of the HarDNet-Lite target detection network on the embedded platform are improved.

The invention discloses a fetal electrocardiogram extraction system based on convolutional encoding-decoding neural network and a method thereof. The system comprises a data collection device, a maternal electrocardiograph component estimation device, and a fetal electrocardiograph component extraction device. The data collection device is for collecting a real abdominal electrical signal of a pregnant woman. The maternal electrocardiograph component estimation device is for using a convolutional encoding-decoding neural network to estimate a maternal electrocardiograph component in the abdominal electrical signal of the pregnant woman; in training, an analog abdominal electrical signal is input into a neural network and a maternal electrocardiograph component in the analog abdominal electrical signal serves as a network label; and in testing, the real abdominal electrical signal of the pregnant woman is input into the neural network and the estimated maternal electrocardiograph component in the abdominal electrical signal is output by the neural network. The fetal electrocardiograph component extraction device is for subtracting the obtained maternal electrocardiograph component from the collected abdominal electrical signal of the pregnant woman to extract a fetal electrocardiograph component from the collected abdominal electrical signal of the pregnant woman. The method comprises the steps of data pre-processing, estimation of the maternal electrocardiograph component and extraction of the fetal electrocardiograph component. Through the technical scheme, the efficiencyand accuracy of fetal electrocardiograph extraction can be effectively improved.

The invention discloses a rapid reconstruction algorithm for a PET image, and the algorithm comprises the following steps: carrying out the one-time iteration of a response curve which accords with atime window through an MLEM algorithm, and obtaining an image which approaches a convergence value; Performing format conversion on the generated image to obtain an RGB image; Classifying the image subjected to format conversion through a deep convolutional neural network to obtain a template of an input image; Carrying out image detail enhancement on the image by utilizing a region growing method; Carrying out point-by-point multiplication on the template subjected to image classification and the template subjected to detail enhancement to obtain a new template; Carrying out point-by-point multiplication on the image obtained after the primary iteration and a new template to obtain an input image of the next iteration; And carrying out second iteration on the image through an MLEM algorithm to complete image reconstruction. According to the method, the problems of low reconstruction efficiency, fuzzy image details, degradation and the like in positron emission tomography image reconstruction are solved.

The invention discloses a distributed optimization large-scale mesh method, which distributively optimizes the initial mesh after mesh reconstruction in three-dimensional reconstruction. The method comprises the following steps: (1) dividing the input initial mesh into a plurality of mesh blocks and sending the mesh blocks to each machine of the cluster; (2) adding boundary constraints on each machine of that clust to distributively simplify and refine the mesh blocks obtained in the step 1); (3) sending the mesh blocks processed in the step (2) on each machine to the master of the cluster, fusing the mesh blocks, and simplifying and refining the mesh blocks to obtain a uniform mesh with no redundant vertices; (4) using multi-layer K-path segmentation algorithm, the aerial images of UAV being segmented into K sub-images; (5) utilizing the image set obtained in the step (4) to distributively restore the detail features of the mesh obtained in the step (3). The invention can generate a smooth mesh with rich detail features from the input initial mesh.