Patent ID: 11880903
Assignee: TSINGHUA UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 7:
8. The method of claim 5, wherein minimizing the Bayesian posterior cost function Ψ adopts a neural network optimization method, comprising:
constructing an image denoising neural network ΩE as an image denoising operator ŝi≡ΩE(zi; θE), i=1:n, where θE is the parameter set of the image denoising neural network;
taking the Bayesian posterior cost function Ψ as the cost function to train the image denoising neural network, and training the image denoising neural network using a dataset with a large number of noisy image samples; and
inputting the noisy image samples into the image denoising neural network ΩE, and acquiring the estimated noiseless images from network outputs as Bayesian denoising results.