Abstract
During the image acquisition process, some level of noise is usually added to the data mainly due to physical limitations of the sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be further processed for noise attenuation without losing details. In this work, we attempt to denoise images using the advantage of sparse-based encoding and deep networks. Experiments on public images corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach concerning some state-of-the-art image denoising approaches.
The authors are grateful to Capes, CNPq grant #306166/2014-3, and FAPESP grants #2014/16250-9, #2014/12236-1, 2016/19403-6, and #2018/21934-5.
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Notes
- 1.
Pictures of objects belonging to 101 categories, and about 40 to 800 images per category.
- 2.
The normalization consists in subtracting all pixel values by 0.5 and dividing them by 0.2.
References
Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2392–2399. IEEE (2012). https://doi.org/10.1109/CVPR.2012.6247952
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising with block-matching and 3D filtering. In: Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, vol. 6064, p. 606414. International Society for Optics and Photonics (2006). https://doi.org/10.1117/12.643267
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006). https://doi.org/10.1109/TIP.2006.881969
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106(1), 59–70 (2007). https://doi.org/10.1016/j.cviu.2005.09.012
Gonzalez, R., Woods, R.: Digital Image Processing, 3rd edn. Prentice-Hall Inc., Upper Saddle River (2006)
Jain, V., Seung, S.: Natural image denoising with convolutional networks. In: Advances in Neural Information Processing Systems, pp. 769–776 (2009)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)
Ngiam, J., Chen, Z., Bhaskar, S.A., Koh, P.W., Ng, A.Y.: Sparse filtering. In: Advances in Neural Information Processing Systems, pp. 1125–1133 (2011)
Schuler, C.J., Burger, H.C., Harmeling, S., Schölkopf, B.: A machine learning approach for non-blind image deconvolution. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1067–1074. IEEE (2013). https://doi.org/10.1109/CVPR.2013.142
Tampere University of Technology: Image and video denoising by sparse 3D transform-domain collaborative filtering. http://www.cs.tut.fi/~foi/GCF-BM3D/. Accessed May 2018
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(Dec), 3371–3408 (2010)
Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: Advances in Neural Information Processing Systems, pp. 341–349 (2012)
Xu, L., Ren, J.S., Liu, C., Jia, J.: Deep convolutional neural network for image deconvolution. In: Advances in Neural Information Processing Systems, pp. 1790–1798 (2014)
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Pires, R.G., Santos, D.S., Souza, G.B., Levada, A.L.M., Papa, J.P. (2019). A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_46
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