Toward Convolutional Blind Denoising of Real Photographs
Open accessAbstract
While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs. The main reason is that their learned models are easy to overfit on the simplified AWGN model which deviates severely from the complicated real-world noise model. In order to improve the generalization ability of deep CNN denoisers, we suggest training a convolutional blind denoising network (CBDNet) with more realistic noise model and real-world noisy-clean image pairs. On the one hand, both signal-dependent noise and in-camera signal processing pipeline is considered to synthesize realistic noisy images. On the other hand, real-world noisy photographs and their nearly noise-free counterparts are also included to train our CBDNet. To further provide an interactive strategy to rectify denoising result conveniently, a noise estimation subnetwork with asymmetric learning to suppress under-estimation of noise level is embedded into CBDNet. Extensive experimental results on three datasets of real-world noisy photographs clearly demonstrate the superior performance of CBDNet over state-of-the-arts in terms of quantitative met- rics and visual quality. The code has been made available at https://github.com/GuoShi28/CBDNet.
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“Specifically, we switched the forensic noise trace extractors in our NoiseDF model in different training sessions and tested the performance of CBDNet (Guo et al. 2019), DnCNN (Zhang et al. 2017), and RIDNet for both in-dataset and cross-dataset evaluations.”
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“CBDNet, QRNN3D .”
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“Another blind denoising model (CBDNet) [25] proposed by Guo et al.”
Method
- Authors
- Guo, S., Yan, Z., Zhang, K., Zuo, W., & Zhang, L.
- Affiliations
- Hong Kong Polytechnic University (HK)
- Harbin Institute of Technology (CN)
- Hong Kong Polytechnic Univ.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 1712-1722
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963725279
- Cited by
- 1,221
- References
- 7
- Citations by year
- 2026: 97 · 2025: 162 · 2024: 189 · 2023: 210 · 2022: 208 · 2021: 195 · 2020: 123 · 2019: 35 · 2018: 2
- FWCI
- 45.72
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61671182, 61672446, 61872118)
- Hong Kong Polytechnic University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- blind image denoising, signal-dependent noise, noise estimation, asymmetric learning, convolutional neural networks
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