Abstract
The image processing has witnessed remarkable progress in image denoising. Nevertheless, restoring the visual quality of the image remains a great challenge. Existing methods might fail to obtain the denoised images with high visual quality since they ignore the potential connection with the high-level feature and result in over-smoothing results. Aiming to research whether high-level feature could influence the performance of denoising task, we propose an end-to-end multi-module neural network architecture, which introduces the combination of the high-level and low-level feature in the training process, for image denoising. In order to guide model preserve structural information efficiently, we introduce a hybrid loss, which is designed to restore details from both pixel and feature space. The experimental results show our method improves the visual quality of images and performs well compared with state-of-the-art methods on three benchmarks.
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References
Aharon, M., Elad, M., Bruckstein, A.: \(rmk\)-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. TSP 54 (2006)
Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: CVPR, June 2012
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space. In: ICIP, September 2007
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. JMLR 12(7), 2121–2159 (2011)
Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. IJCV 111, 98–136 (2015)
Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: CVPR, June 2014
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, June 2016
Jain, V., Seung, H.S.: Natural image denoising with convolutional networks. In: NIPS (2008)
Johnson, J., Alahi, A., Li, F.: Perceptual losses for real-time style transfer and super-resolution. CoRR (2016)
Lefkimmiatis, S.: Universal denoising networks: a novel CNN architecture for image denoising. In: CVPR (2018)
Liu, D., Wen, B., Liu, X., Wang, Z., Huang, T.S.: When image denoising meets high-level vision tasks: a deep learning approach. In: IJCAI (2018)
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: ICCV. IEEE (2009)
Mao, X.J., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. CoRR (2016)
Niknejad, M., Bioucas-Dias, J.M., Figueiredo, M.A.T.: Class-specific poisson denoising by patch-based importance sampling. arXiv preprint arXiv:1706.02867 (2017)
Paszke, A., et al.: Automatic differentiation in PyTorch (2017)
Remez, T., Litany, O., Giryes, R., Bronstein, A.M.: Class-aware fully convolutional gaussian and poisson denoising. TIP 27(11), 5707–5722 (2018)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)
Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. TIP 15(11), 3440–3451 (2006)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Szegedy, C., et al.: Going deeper with convolutions. In: CVPR, pp. 1–9 (2015)
Wang, T., Sun, M., Hu, K.: Dilated deep residual network for image denoising. In: ICTAI, November 2017
Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: NIPS (2012)
Yang, Q., et al.: Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. T-MI 37(6), 1348–1357 (2018)
Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: CVPR, July 2017
Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. TIP 26, 3142–3155 (2017)
Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. TIP 27(9), 4608–4622 (2018)
Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: ICCV. IEEE (2011)
Acknowledgement
This work has been supported by HGJ, HJSW and Research & Development plan of Shaanxi Province (Program No. 2017ZDXM-GY-094, 2015KTZDGY04-01).
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Li, F., Kuang, N., Zheng, J., Wei, Q., Xi, Y., Guo, Y. (2020). Combining Multi-level Loss for Image Denoising. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_41
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DOI: https://doi.org/10.1007/978-3-030-39431-8_41
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