关键词:
random noise
stationary wavelet packet transform
deep learning
noise level map
Huber norm
摘要:
Many traditional denoising methods,such as Gaussian filtering,tend to blur and lose details or edge information while reducing *** stationary wavelet packet transform is a multi-scale and multi-band analysis *** with the stationary wavelet transform,it can suppress high-frequency noise while preserving more edge *** learning has significantly progressed in denoising ***,a residual network;FFDNet,an efficient,fl exible network;U-NET,a codec network;and GAN,a generative adversative network,have better denoising effects than BM3D,the most popular conventional denoising ***,SWP_hFFDNet,a random noise attenuation network based on the stationary wavelet packet transform(SWPT)and modified FFDNet,is *** network combines the advantages of SWPT,Huber norm,and *** addition,it has three characteristics:First,SWPT is an eff ective featureextraction tool that can obtain low-and high-frequency features of different scales and frequency ***,because the noise level map is the input of the network,the noise removal performance of diff erent noise levels can be ***,the Huber norm can reduce the sensitivity of the network to abnormal data and enhance its *** network is trained using the Adam algorithm and the BSD500 dataset,which is augmented,noised,and decomposed by *** and actual data processing results show that the denoising eff ect of the proposed method is almost the same as those of BM3D,DnCNN,and FFDNet networks for low ***,for high noise,the proposed method is superior to the aforementioned networks.