6results about How to "Improve denoising efficiency" patented technology

The application discloses a deep learning point target denoising method for remote sensing task planning and belongs to the technical field of mega remote sensing constellation task planning.The application is aimed at the problem that the existing method is slow in calculating the satellite imaging time window for a point target in mega remote sensing constellation task planning and is time-consuming.The application comprises the following steps: orbit recursion is carried out according to the initial coordinate position of each satellite to determine the eccentric anomaly and the true anomaly at a given time;the orbit six numbers and the longitude, latitude and height of the task target are expressed as coordinate vectors in the ECEF coordinate system to calculate the imaging side swing angle;the golden section search method is adopted to determine the imaging time window;the imaging time and the imaging side swing angle are compared to determine the mark L and obtain a training data set;the point task denoising neural network is trained by using the network training data set, and the cross-entropy loss function is used for training and fitting of the neural network to obtain the trained point task denoising neural network.The application is used for point target denoising in remote sensing task planning.

The invention discloses a method for accelerating complex scene ray tracing rendering, and relates to the technical field of computer graphic analysis. The method comprises the following steps: carrying out rapid ray tracing rendering on a complex scene by utilizing a distributed rendering cluster, and improving the calculation efficiency when the distributed rendering cluster renders a frame of complex scene by matching a load balancing strategy with the distributed rendering cluster; a frame of rendering result image generated by the distributed rendering cluster and corresponding auxiliary information are input into a multi-scale denoising device for post-processing of filtering denoising and time anti-aliasing, the sense of reality of the rendering result image is improved, and the rendering result image output after processing of the multi-scale denoising device is the final high-sense-of-reality rendering result image. The invention aims at effectively carrying out vivid rendering on a complex scene, and improving the rendering efficiency and effectively denoising while keeping the rendering effect.

The invention provides an image denoising model generation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring an initial network model; wherein the initial network model is a preset diffusion model, and the initial network model is used for denoising the image; pruning the initial network model to obtain a first network model, and distilling the initial network model to obtain a second network model; and generating an image denoising model according to the first network model and the second network model. According to the method provided by the invention, the speed of the image denoising process is improved, and the quality of the denoised image is improved.

The embodiment of the application provides a kind of image denoising method, device, system, edge device and storage medium, the method comprises: obtaining to be denoised image;With preset multi-head attention mechanism module, the first feature matrix of to-be-denosed image is extracted, wherein, preset multi-head attention mechanism module includes self-attention mechanism and channel attention mechanism;First feature matrix is up-sampled, and second feature matrix is obtained;Based on second feature matrix, the denoised image corresponding to to-be-denosed image is generated. The technical scheme provided in the embodiment of the application can improve the image denoising efficiency, reduce the consumption of computing resources, and then reduce the performance requirements of the device.

The invention discloses a debugging method and system of a dielectric filter, relates to the technical field of filtering and noise reduction, is used for solving the problems that noise levels of local areas of an image are different, and image details are lost due to the fact that the same algorithm is used for integrally denoising the image, and comprises the steps of image sample source recognition and operation. Obtaining the storage times and the compression times of the image sample, performing preliminary analysis on the quality reduction degree of the image sample according to the storage times and the compression times of the image sample, detecting a histogram of the image sample, and determining the number of abnormal peak values; the denoising difficulty of the image samples is determined by synthesizing the quality reduction degree of the image samples and the number of histogram abnormal peak values, classification screening is carried out, and when the denoising difficulty of the image samples is large, region division is carried out on the image samples; the noise quantity of the divided areas is calculated by detecting the pixel change rate and the area pixel value of the divided areas through a lasso regression algorithm, and different filtering algorithms are selected for denoising according to the noise quantity of the divided areas.

The present application discloses an image denoising method, including performing four-dimensional contrast time imaging on a four-dimensional ultrasound image sequence to be processed to obtain a four-dimensional contrast image sequence, wherein each four-dimensional contrast image in the four-dimensional contrast image sequence is a color-based time contrast image; determining a target four-dimensional contrast image where the noise is located from the four-dimensional contrast image sequence, and determining the noise color in the target four-dimensional contrast image and the noise cropping area where the noise color is located; constructing a color cropping template using the noise color and the noise cropping area; denoising the four-dimensional contrast image sequence using the color cropping template; the image denoising method can achieve accurate segmentation of noise information and tissue information in image data, and effectively improve the accuracy of image denoising. The present application also discloses an image denoising device, equipment and computer-readable storage medium, all of which have the above-mentioned beneficial effects.