Deep Orthogonal Transform Feature for Image Denoising
- Authors
- Shin, Yoon-Ho; Park, Min-Je; Lee, Oh-Young; Kim, Jong-Ok
- Issue Date
- 2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Image denoising; deep learning for image denoising; orthogonal transform; multi-input network; PCA; wavelet transform
- Citation
- IEEE ACCESS, v.8, pp.66898 - 66909
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 8
- Start Page
- 66898
- End Page
- 66909
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/58929
- DOI
- 10.1109/ACCESS.2020.2986827
- ISSN
- 2169-3536
- Abstract
- 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 noise-separable 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.
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