The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising
Open accessAbstract
Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. To address these issues, we present a fast and flexible denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. The proposed FFDNet works on downsampled subimages, achieving a good trade-off between inference speed and denoising performance. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including (i) the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network, (ii) the ability to remove spatially variant noise by specifying a non-uniform noise level map, and (iii) faster speed than benchmark BM3D even on CPU without sacrificing denoising performance. Extensive experiments on synthetic and real noisy images are conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The results show that FFDNet is effective and efficient, making it highly attractive for practical denoising applications.
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Cites
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BackgroundMethodInfluentialSelf-citationLearning Deep CNN Denoiser Prior for Image Restoration
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Cited by
Pre-Trained Image Processing Transformer
Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., & Gao, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01212 · Cited by 2,308
Toward Convolutional Blind Denoising of Real Photographs
Guo, S., Yan, Z., Zhang, K., Zuo, W., & Zhang, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00181 · Cited by 1,221
Self-citationDeep Learning on Image Denoising: An overview
Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., & Lin, C.-W. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.07.025 · Cited by 1,091
Self-citationReal Image Denoising With Feature Attention
Anwar, S., & Barnes, N. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00325 · Cited by 784
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
Zhang, K., Liang, J., Van Gool, L., & Timofte, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00475 · Cited by 760
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Metadata
- Authors
- Zhang, K., Zuo, W., & Zhang, L.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2018-09
- Volume
- 27
- Issue
- 9
- Pages
- 4608-4622
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2764207251
- Cited by
- 2,678
- References
- 12
- FWCI
- 86.36
- Type
- journal article
- Language
- en
- Open access
- green
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