公开(公告)号：US12045961B2

公开(公告)日：2024-07-23

申请号：US18489876

申请日：2023-10-19

Applicant: ZHEJIANG LAB

Inventor： Jingsong Li ,  Jinnan Hu ,  Peijun Hu ,  Yu Tian ,  Tianshu Zhou

IPC: G06T5/70 ,  G06T5/10

CPC classification number: G06T5/70 ,  G06T5/10 ,  G06T2207/20064 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Disclosed is an image denoising method and apparatus based on wavelet high-frequency channel synthesis. Image data are expanded to a plurality of frequency-domain channels, a plurality of “less-noise” channels and a plurality of “more-noise” channels are grouped through a noise-sort algorithm, and a denoising submodule and a synthesis submodule based on style transfer are combined to form a generative network. A discriminative network is established to add a constraint to the global loss function. After iteratively training the GAN model described above, the denoised image data can be obtained through wavelet inverse transformation. The disclosed algorithm can effectively solve the problem of “blurring” and “loss of details” introduced by traditional filtering or CNN-based deep learning methods, which is especially suitable for noise-overwhelmed image data or high dimensional image data.