Deep Image Denoising With Adaptive Priors

Image denoising methods using deep neural networks have achieved a great progress in the image restoration. However, the recovered images restored by these deep denoising methods usually suffer from severe over-smoothness, artifacts, and detail loss. To improve the quality of restored images, we fir...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2022-08, Vol.32 (8), p.5124-5136
Main Authors: Jiang, Bo, Lu, Yao, Wang, Jiahuan, Lu, Guangming, Zhang, David
Format: Article
Language:English
Subjects:
adaptive priors
Adaptive systems
Artificial neural networks
Coders
Decoding
Image denoising
Image quality
Image reconstruction
Image restoration
Machine learning
Neural networks
Noise measurement
Noise reduction
over-fitting
over-smoothness
Regularization
regularization priors
Smoothness
supplemental priors
Training
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Summary:Image denoising methods using deep neural networks have achieved a great progress in the image restoration. However, the recovered images restored by these deep denoising methods usually suffer from severe over-smoothness, artifacts, and detail loss. To improve the quality of restored images, we first propose Supplemental Priors (SP) method to adaptively predict depth-directed and sample-directed prior information for the reconstruction (decoder) networks. Furthermore, the over-parameterized deep neural networks and too precise supplemental prior information may cause an over-fitting, restricting the performance promotion. To improve the generalization of denoising networks, we further propose Regularization Priors (RP) method to flexibly learn depth-directed and dataset-directed regularization noise for the retrieving (encoder) networks. By respectively integrating the encoder and decoder with these plug-and-play RP block and SP block, we propose the final Adaptive Prior Denoising Networks, called APD-Nets. APD-Nets is the first attempt to simultaneously regularize and supplement denoising networks from the adaptive priors' view with drawing learning-based mechanism into producing adaptive regularization noise and supplemental information. Extensive experiment results demonstrate our method significantly improves the generalization of denoising networks and the quality of restored images with greatly outperforming the traditional deep denoising methods both quantitatively and visually. The code will be released at https://github.com/JiangBoCS/APD-Nets .
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2022.3149518