2 results about "Multiplicative noise" patented technology

The present application belongs to the technical field of remote sensing image processing, and provides a SAR image denoising method and device based on a logarithmic domain diffusion model. The denoising method comprises: preprocessing: converting an original SAR amplitude image to a logarithmic domain to obtain a starting input image for a diffusion process; forward diffusion: constructing a forward diffusion path based on a non-central Gaussian distribution, simulating a noise injection process, and generating a noisy image sequence; neural network model noise prediction: performing feature extraction and fusion on the noisy image and physical metadata through a pre-constructed neural network model, and outputting a noise residual prediction result; backward sampling reconstruction: iteratively sampling using the noise residual prediction result to reconstruct a denoised SAR image. The denoising method solves the distribution mismatch problem of traditional diffusion models for SAR multiplicative noise, improves the denoising precision, and preserves the image texture and structure information.

The application relates to a distributed fusion estimation method with sensor saturation and multiplicative noise, and relates to the technical field of sensor network information fusion estimation. A dynamic model of a multi-sensor linear random system with sensor saturation and multiplicative noise is established; state estimation is performed on the dynamic model; a prediction intermediate matrix of a local estimator of each node at a time t is calculated; correction parameters of each node in a sensor network at the time t are calculated; a local estimator of the node at the time t is obtained; whether a total time length is reached is judged; if yes, the process is ended; otherwise, the next step is executed; an estimation error constraint matrix of each node at the time t is calculated; and fusion estimation and fusion estimation error covariance of the sensor network at the time t are calculated. The influence of the sensor saturation and the multiplicative noise on the fusion estimation performance is considered simultaneously, the method is easy to realize on line, and the fusion estimation precision is high.