Multi-source information fusion image denoising method based on fractional modeling

Most of the image denoising methods are based on the integer order modeling of multi-source information fusion method. Because of the lack of research on the method of fractional modeling in the previous methods, the denoising effect of the image is poor. Based on this, this project is based on fractional order modeling, combined with two-dimensional fractional Kalman filter, and denoising the image under the influence of multi-sensor information fusion. First, this paper establishes two-dimensional fractional order linear discrete system in 2-D image differential state space model, and then uses the Kalman filter algorithm in multi-sensor information fusion function, denoises the image, and demonstrate the superiority of the proposed algorithm. The simulation results show that the multi-sensor information fusion algorithm based on the fractional order model is effective in improving the peak signal-to-noise ratio (PSNR) and the noise reduction of the image in two aspects.