The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Image denoising using deep CNN with batch renormalization
Abstract
Deep convolutional neural networks (CNNs) have attracted great attention in the field of image denoising. However, there are two drawbacks: (1) it is very difficult to train a deeper CNN for denoising tasks, and (2) most of deeper CNNs suffer from performance saturation. In this paper, we report the design of a novel network called a batch-renormalization denoising network (BRDNet). Specifically, we combine two networks to increase the width of the network, and thus obtain more features. Because batch renormalization is fused into BRDNet, we can address the internal covariate shift and small mini-batch problems. Residual learning is also adopted in a holistic way to facilitate the network training. Dilated convolutions are exploited to extract more information for denoising tasks. Extensive experimental results show that BRDNet outperforms state-of-the-art image-denoising methods. The code of BRDNet is accessible at http://www.yongxu.org/lunwen.html.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
Lin, G., Milan, A., Shen, C., & Reid, I. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.549 · Cited by 3,287
Weighted Nuclear Norm Minimization with Application to Image Denoising
Gu, S., Zhang, L., Zuo, W., & Feng, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.366 · Cited by 2,316 · cited 10×
“Several state-of-the-art methods for gray synthetic noisy image denoising, such as BM3D [11], WNNM [19], MLP [4], trainable nonlinear reaction diffusion (TNRD)[7], expected patch log likelihood (EPLL) [68], cascade of shrinkage fields (CS-F) [50], DnCNN [61], IRCNN [62], and FFDNet [63] are chosen for comparison with t…”
BackgroundMethodInfluentialSelf-citationLearning Deep CNN Denoiser Prior for Image Restoration
Zhang, K., Zuo, W., Gu, S., & Zhang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.300 · Cited by 2,116 · cited 6×
“A good denoiser should realize a tradeoff between PSNR and running time [62].”
BackgroundMethodInfluentialSelf-citation
Cited by
Deep 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-citationAttention-guided CNN for image denoising
Tian, C., Xu, Y., Li, Z., Zuo, W., Fei, L., & Liu, H. (2020) · Neural Networks · doi:10.1016/j.neunet.2019.12.024 · Cited by 688
Self-citationEfficient Long-Range Attention Network for Image Super-Resolution
Zhang, X., Zeng, H., Guo, S., & Zhang, L. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19790-1_39 · Cited by 620
Chen, K., Pu, X., Ren, Y., Qiu, H., Lin, F., & Zhang, S. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3034752 · Cited by 561
All-In-One Image Restoration for Unknown Corruption
Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., & Peng, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01693 · Cited by 432
Metadata
- Authors
- Tian, C., Xu, Y., & Zuo, W.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 121
- Pages
- 461-473
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2971719842
- Cited by
- 589
- References
- 12
- FWCI
- 27.26
- Type
- journal article
- Language
- en
- Open access
- closed
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