Deep Burst Denoising

Author:

Godard Clément,Matzen Kevin,Uyttendaele Matt

Publisher

Springer International Publishing

Reference50 articles.

1. Agostinelli, F., Anderson, M.R., Lee, H.: Adaptive multi-column deep neural networks with application to robust image denoising. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 1493–1501. Curran Associates, Inc. (2013)

2. Borman, S., Stevenson, R.L.: Super-resolution from image sequences-a review. In: Proceedings of 1998 Midwest Symposium on Circuits and Systems, pp. 374–378. IEEE (1998)

3. 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)

4. 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)

5. Chaitanya, C.R.A., Kaplanyan, A.S., Schied, C., Salvi, M., Lefohn, A., Nowrouzezahrai, D., Aila, T.: Interactive reconstruction of monte carlo image sequences using a recurrent denoising autoencoder. ACM Trans. Graph. (TOG) 36(4), 98 (2017)

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