Abstract
We propose a novel direction to improve the denoising quality of filtering-based denoising algorithms in real time by predicting the best filter parameter value using a Convolutional Neural Network (CNN). We take the use case of BM3D, the state-of-the-art filtering-based denoising algorithm, to demonstrate and validate our approach. We propose and train a simple, shallow CNN to predict in real time, the optimum filter parameter value, given the input noisy image. Each training example consists of a noisy input image (training data) and the filter parameter value that produces the best output (training label). Both qualitative and quantitative results using the widely used PSNR and SSIM metrics on the popular BSD68 dataset show that the CNN-guided BM3D outperforms the original, unguided BM3D across different noise levels. Thus, our proposed method is a CNN-based improvement on the original BM3D which uses a fixed, default parameter value for all images.
Supported by NSERC Discovery Grant and DND Supplement.
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Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/
Bashar, F., El-Sakka, M.R.: BM3D image denoising using learning-based adaptive hard thresholding. In: Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS - Science and and Technology Publications (2016). https://doi.org/10.5220/0005787202040214
Beheshti, S., Hashemi, M., Zhang, X.P., Nikvand, N.: Noise invalidation denoising. IEEE Trans. Signal Process. 58(12), 6007–6016 (2010). https://doi.org/10.1109/TSP.2010.2074199
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 60–65. IEEE (2005)
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)
Chatterjee, P., Milanfar, P.: Clustering-based denoising with locally learned dictionaries. IEEE Trans. Image Process. 18(7), 1438–1451 (2009)
Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1256–1272 (2017). https://doi.org/10.1109/TPAMI.2016.2596743
Chollet, F., et al.: Keras (2015). https://keras.io
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007). https://doi.org/10.1109/TIP.2007.901238
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: BM3D image denoising with shape-adaptive principal component analysis. In: Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS 2009), April 2009
Dai, L., Zhang, Y., Li, Y.: BM3D image denoising algorithm with adaptive distance hard-threshold. Int. J. Signal Process. Image Process. Pattern Recognit. 6(6), 41–50 (2013). https://doi.org/10.14257/ijsip.2013.6.6.04
Egiazarian, K., Danielyan, A., Ponomarenko, N., Foi, A., Ieremeiev, O., Lukin, V.: BM3D-HVS: content-adaptive denoising for improved visual quality. Electron. Imaging 2017(13), 48–55 (2017). https://doi.org/10.2352/issn.2470-1173.2017.13.dpmi-083
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)
Elahi, P., Beheshti, S., Hashemi, M.: BM3D mridenoising equipped with noise invalidation technique. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6612–6616, May 2014
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Honzátko, D., Kruliš, M.: Accelerating block-matching and 3D filtering method for image denoising on GPUs. J. Real-Time Image Process. (2017). https://doi.org/10.1007/s11554-017-0737-9
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lebrun, M.: An analysis and implementation of the BM3D image denoising method. Image Process. Line 2, 175–213 (2012). https://doi.org/10.5201/ipol.2012.l-bm3d
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 689–696. ACM (2009)
Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Advances in Neural Information Processing Systems, pp. 2802–2810 (2016)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the 8th International Conference on Computer Vision, vol. 2, pp. 416–423, July 2001
Olmos, A., Kingdom, F.A.A.: A biologically inspired algorithm for the recovery of shading and reflectance images. Perception 33(12), 1463–1473 (2004). https://doi.org/10.1068/p5321
Pltz, T., Roth, S.: Benchmarking denoising algorithms with real photographs. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2750–2759, July 2017. https://doi.org/10.1109/CVPR.2017.294
Romano, Y., Isidoro, J., Milanfar, P.: RAISR: rapid and accurate image super resolution. IEEE Trans. Comput. Imaging 3(1), 110–125 (2017)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D Nonlinear Phenom. 60(1–4), 259–268 (1992)
Sifre, L.: Rigid-motion scattering for image classification. Ph.D. thesis, Ecole Polytechnique, October 2014
Sun, L., Hays, J.: Super-resolution from internet-scale scene matching. In: 2012 IEEE International Conference on Computational Photography (ICCP), pp. 1–12, April 2012. https://doi.org/10.1109/ICCPhot.2012.6215221
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, 3371–3408 (2010)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861
Weiss, Y., Freeman, W.T.: What makes a good model of natural images? In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)
Yang, D., Sun, J.: BM3D-Net: a convolutional neural network for transform-domain collaborative filtering. IEEE Signal Process. Lett. 25(1), 55–59 (2018). https://doi.org/10.1109/LSP.2017.2768660
Ye, X., Wang, L., Xing, H., Huang, L.: Denoising hybrid noises in image with stacked autoencoder. In: 2015 IEEE International Conference on Information and Automation, pp. 2720–2724, August 2015. https://doi.org/10.1109/ICInfA.2015.7279746
Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)
Zhu, F., Chen, G., Heng, P.A.: From noise modeling to blind image denoising. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 420–429, June 2016. https://doi.org/10.1109/CVPR.2016.52
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Mukherjee, S., Kottayil, N.K., Sun, X., Cheng, I. (2019). CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter Performance. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11662. Springer, Cham. https://doi.org/10.1007/978-3-030-27202-9_10
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