Image denoising using deep CNN with batch renormalization

Barbu, 2009 Barbu, 2009, Training an active random field for real-time image denoising, IEEE Transactions on Image Processing, 18, 2451, 10.1109/TIP.2009.2028254 Beck, 2009, Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems, IEEE Transactions on Image Processing, 18, 2419, 10.1109/TIP.2009.2028250 Burger, 2012, Image denoising: Can plain neural networks compete with BM3D?, 2392 Chambolle, 2004, An algorithm for total variation minimization and applications, Journal of Mathematical Imaging and Vision, 20, 89 Chen, J., Chen, J., Chao, H., & Yang, M. (2018). Image blind denoising with generative adversarial network based noise modeling. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3155–3164). Chen, 2017, Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1256, 10.1109/TPAMI.2016.2596743 Chollet, 2015 Chu, 2017, Rich features and precise localization with region proposal network for object detection, 605 Cruz, C., Foi, A., Katkovnik, V., & Egiazarian, K. (2018). Nonlocality-reinforced convolutional neural networks for image denoising. arXiv preprint arXiv:1803.02112. Dabov, 2007, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Transactions on Image Processing, 16, 2080, 10.1109/TIP.2007.901238 Dong, 2016, Image super-resolution using deep convolutional networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295, 10.1109/TPAMI.2015.2439281 Dong, W., Wang, P., Yin, W., Shi, G., Wu, F., & Lu, X. (2018). Denoising prior driven deep neural network for image restoration. arXiv preprint arXiv:1801.06756. Dong, 2013, Nonlocally centralized sparse representation for image restoration, IEEE Transactions on Image Processing, 22, 1620, 10.1109/TIP.2012.2235847 Du, 2019, An improved quantum-behaved particle swarm optimization for endmember extraction, IEEE Transactions on Geoscience and Remote Sensing, 10.1109/TGRS.2019.2903875 Du, 2017, Stacked convolutional denoising auto-encoders for feature representation, IEEE Transactions on Cybernetics, 47, 1017, 10.1109/TCYB.2016.2536638 Fei, 2018, Feature extraction methods for palmprint recognition: A survey and evaluation, IEEE Transactions on Systems, Man, and Cybernetics: Systems Franzen, 1999 Gu, S., Zhang, L., Zuo, W., & Feng, X. (2014). Weighted nuclear norm minimization with application to image denoising. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2862–2869). Guo, 2017, Face recognition using both visible light image and near-infrared image and a deep network, CAAI Transactions on Intelligence Technology, 2, 39, 10.1016/j.trit.2017.03.001 He, 2014, Spatial pyramid pooling in deep convolutional networks for visual recognition, 346 He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision (pp. 1026–1034),. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778). Ioffe, 2017, Batch renormalization: Towards reducing minibatch dependence in batch-normalized models, 1945 Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167. Jiao, 2017, Formresnet: formatted residual learning for image restoration, 1034 Kim, J., Kwon Lee, J., & Mu Lee, K. (2016). Deeply-recursive convolutional network for image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1637–1645). Kim, J., Kwon Lee, J., & Mu Lee, K. Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1646–1654). Kingma, 2014, Adam: A method for stochastic optimization, Computer Science Kligvasser, I., Shaham, T. R., & Michaeli, T. (2017). xUnit: Learning a Spatial activation function for efficient image restoration. arXiv preprint arXiv:1711.06445. Kokkinos, F., & Lefkimmiatis, S. (2018). Iterative residual network for deep joint image demosaicking and denoising. arXiv preprint arXiv:1807.06403. Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097 Lai, 2017, Deep laplacian pyramid networks for fast and accurate superresolution, vol. 2, 5 Latif, 2018, Deep CNN based MR image denoising for tumor segmentation using watershed transform, International Journal of Engineering & Technology, 7, 37, 10.14419/ijet.v7i2.3.9964 Lebrun, 2015, Multiscale image blind denoising, IEEE Transactions on Image Processing, 24, 3149, 10.1109/TIP.2015.2439041 Lebrun, 2015, The noise clinic: a blind image denoising algorithm, Image Processing on Line, 5, 1, 10.5201/ipol.2015.125 Li, 2012, Group-sparse representation with dictionary learning for medical image denoising and fusion, IEEE Transactions on Biomedical Engineering, 59, 3450, 10.1109/TBME.2012.2217493 Lin, 2017, RefineNet: Multi-path refinement networks for high-resolution semantic segmentation, Vol. 1, 5 Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431–3440). Lu, 2018, Structurally incoherent low-rank nonnegative matrix factorization for image classification, IEEE Transactions on Image Processing, 27, 5248, 10.1109/TIP.2018.2855433 Ma, 2017, Waterloo exploration database: New challenges for image quality assessment models, IEEE Transactions on Image Processing, 26, 1004, 10.1109/TIP.2016.2631888 Mairal, 2009, Non-local sparse models for image restoration, 2272 Malfait, 1997, Wavelet-based image denoising using a Markov random field a priori model, IEEE Transactions on Image Processing, 6, 549, 10.1109/83.563320 Nam, S., Hwang, Y., Matsushita, Y., & Joo Kim, S. (2016). A holistic approach to cross-channel image noise modeling and its application to image denoising. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1683–1691). Pascanu, R., Mikolov, T., & Bengio, Y. (2013). On the difficulty of training recurrent neural networks. In International conference on machine learning (pp. 1310–1318). Plotz, T., & Roth, S. (2017). Benchmarking denoising algorithms with real photographs. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1586–1595). Ren, 2018, Nonlocal similarity modeling and deep CNN gradient prior for super resolution, IEEE Signal Processing Letters, 25, 916, 10.1109/LSP.2018.2829766 Roth, 2009, Fields of experts, International Journal of Computer Vision, 82, 205, 10.1007/s11263-008-0197-6 Rudin, 1992, Nonlinear total variation based noise removal algorithms, Physica D. Nonlinear Phenomena, 60, 259, 10.1016/0167-2789(92)90242-F Schmidt, U., & Roth, S. (2014). Shrinkage fields for effective image restoration. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2774–2781). Si-Yao, L., Ren, D., Hu, Z., Li, J., Yin, Q., & Guo, P. (2018). A concatenated residual network for image deblurring. arXiv preprint arXiv:1804.06042. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., & Anguelov, D., et al. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–9). Tai, 2017, Image super-resolution via deep recursive residual network, 1, 5 Wang, Z., Liu, D., Yang, J., Han, W., & Huang, T. (2015). Deep networks for image super-resolution with sparse prior. In Proceedings of the IEEE international conference on computer vision (pp. 370–378). Wang, T., Sun, M., & Hu, K. (2017). Dilated residual network for image denoising. arXiv preprint arXiv:1708.05473. Wen, 2018, Low-rank representation with adaptive graph regularization, Neural Networks, 10.1016/j.neunet.2018.08.007 Wen, 2018, Inter-class sparsity based discriminative least square regression, Neural Networks, 102, 36, 10.1016/j.neunet.2018.02.002 Xu, J., Li, H., Liang, Z., Zhang, D., & Zhang, L. (2018). Real-world noisy image denoising: A new benchmark. arXiv preprint arXiv:1804.02603. Xu, 2018, External prior guided internal prior learning for real-world noisy image denoising, IEEE Transactions on Image Processing, 27, 2996, 10.1109/TIP.2018.2811546 Yu, F., & Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., & Fu, Y. (2018). Residual dense network for image super-resolution. In The IEEE conference on computer vision and pattern recognition . Zhang, 2011, Color demosaicking by local directional interpolation and nonlocal adaptive thresholding, Journal of Electronic Imaging, 20 Zhang, 2017, Image restoration: From sparse and low-rank priors to deep priors [lecture notes], IEEE Signal Processing Magazine, 34, 172, 10.1109/MSP.2017.2717489 Zhang, 2017, Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising, IEEE Transactions on Image Processing, 26, 3142, 10.1109/TIP.2017.2662206 Zhang, 2017, Learning deep CNN denoiser prior for image restoration, Vol. 2 Zhang, 2018, FFDNet: Toward a fast and flexible solution for CNN based image denoising, IEEE Transactions on Image Processing Zhang, 2018, Learning a single convolutional super-resolution network for multiple degradations, 6 Zoran, 2011, From learning models of natural image patches to whole image restoration, 479 Zuo, 2014, Gradient histogram estimation and preservation for texture enhanced image denoising, IEEE Transactions on Image Processing, 23, 2459, 10.1109/TIP.2014.2316423