5results about How to "Improve denoising performance" patented technology

The invention relates to a point cloud denoising method of a photon counting laser radar and an intelligent denoising system, and belongs to the technical field of photon laser radar data processing. The technical problems that the performance of a fixed parameter denoising method is seriously unbalanced under different ground feature and complexity scenes, and high denoising rate and high signal fidelity cannot be considered at the same time are solved. Synchronously acquiring an optical image and an original photon point cloud of the target area; identifying the dominant ground feature type of the laser spot area through real-time semantic segmentation; calculating scene complexity based on the point cloud data and determining the grade of the scene complexity; querying a preset two-dimensional parameter mapping table according to the combination of the surface feature type and the scene complexity level, and dynamically obtaining an optimal denoising parameter set; and finally, the parameter set is utilized to drive adaptive denoising processing, and signal points and noise points are distinguished by calculating the local density and direction consistency of the points and constructing a two-dimensional discrimination space. The method is mainly used for real-time processing platforms such as unmanned aerial vehicles, and the point cloud denoising precision, the signal retention rate and the topographic feature fidelity are remarkably improved.

The invention provides a low-dose CT image self-supervision denoising method based on axial structure consistency, and belongs to the technical field of medical image processing and deep learning. The method comprises the following steps of: inputting a DICOM (Digital Imaging and Communications in Medicine) image sequence scanned by original LDCT (Level Discrete Cosine Transform), extracting three continuous axial slices to generate a triple sample data set, and calculating an axial difference image of two adjacent slices to construct a two-tuple data set; the method comprises the following steps: after constructing a double-branch neural network framework of a denoising network and a difference enhancement network, respectively outputting a denoised triple and axial structure offset estimation, and calculating a pseudo-supervision signal; and on the basis of the pseudo-supervised signal and the denoised triple, calculating by constructing a self-supervised joint loss optimization function to obtain a model for denoising the low-dose CT image. According to the method, the problems of axial structure dislocation, high-frequency detail loss, excessive smoothness and the like in an existing self-supervised denoising method are effectively solved, and the structural consistency and detail fidelity of the image are remarkably improved while noise suppression is ensured.

The application discloses a kind of meridian tire X-ray image disease flaw automatic detection method based on improved YOLO-v5 model, belong to meridian tire detection technical field, including following steps: step 1, meridian tire X-ray image is collected and is segmented processing, uniform resolution, model training sample data are made;Step 2, for the image strip, block missing caused by X-ray machine instability, thereby affecting the detection effect Problem, image restoration processing is carried out;Step 3, design improved YOLO-v5 model, including increasing the fourth detection layer, increase attention module, and improve loss function;Step 4, using meridian tire disease flaw data carries out model training;Step 5, using the model of training completion carries out actual application scene tire disease flaw detection.The application is a kind of automatic detection method, based on improved model can automatically identify multiple disease flaws, and different disease flaws are detected and classified, and detection efficiency and accuracy are higher.

The application discloses a kind of mobile device image denoising methods based on double-branch residual sparse network, belong to image processing technical field, which includes: residual sparse module, by mixed dilated convolution and residual connection capture local feature in image, while reducing the number of parameters and computational complexity of model;Attention guiding residual sparse module introduces channel attention and pixel attention mechanism on the basis of residual sparse block to adjust the weight of feature map, pay attention to important area in image, improve denoising effect and image quality;Feature fusion module, by adding double-branch output after residual module processing, combined with attention mechanism and activation function, realize high-frequency detail preservation and noise suppression.The application carries out image denoising in unmanned aerial vehicle and other resource-limited scenarios, solves the problems of large parameter quantity, low computational efficiency, insufficient detail preservation and other problems of existing denoising model.

This invention belongs to the field of image processing technology and discloses an image denoising method based on a dual-output regularized network using a composite Jacobi loss. The method includes: first, performing data augmentation, resizing, and normalization on the training dataset and the image to be denoised to generate a noisy image; then, constructing a dual-output FFDNet regularized network, which integrates four functional modules: noise awareness (NA), multi-scale feature fusion (MSFF), hybrid attention (CBAM), and global-local feature enhancement (GFMM-LFEM). This network simultaneously outputs intermediate feature maps and reconstructed images; next, designing a composite loss function including an MSE fidelity term, an L1 regularization term, and a Jacobi regularization term, constraining the monotonicity of pixel changes through the Jacobi matrix; subsequently, training the dual-output FFDNet regularized network using the Adam optimizer and saving the optimal model on the validation set; finally, embedding the trained network as a nearest neighbor denoising module into the PnP-ISTA iterative framework, and iteratively optimizing to output the final denoised image.