Deep Orthogonal Transform Feature for Image Denoising

Recently, CNN-based image denoising has been investigated and shows better performance than conventional vision based techniques. However, there are still a couple of limits that are weak partly in restoring image details like textured regions or produce other artifacts. In this paper, we introduce...

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Bibliographic Details
Published in:IEEE access 2020-01, Vol.8, p.1-1
Main Authors: Shin, Yoon-Ho, Park, Min-Je, Lee, Oh-Young, Kim, Jong-Ok
Format: Article
Language:English
Subjects:
Deep learning for image denoising
Image denoising
Image restoration
Multi-input network
Noise measurement
Noise reduction
Orthogonal transform
PCA
Principal component analysis
Wavelet domain
Wavelet transform
Wavelet transforms
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Summary:Recently, CNN-based image denoising has been investigated and shows better performance than conventional vision based techniques. However, there are still a couple of limits that are weak partly in restoring image details like textured regions or produce other artifacts. In this paper, we introduce noiseseparable orthogonal transform features into a neural denoising framework. We specifically choose wavelet and PCA as an orthogonal transform, which achieved a good denoising performance conventionally. In addition to spatial image signals, the orthogonal transform features (OTFs) are fed into a denoising network. For the guide of the denoising process, we also concatenate OTFs from the image denoised by the existing method. This can play a role of prior for learning a denoising process. It has been confirmed that our proposed multi-input network can achieve better denoising performance than other single-input networks.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2986827