Abstract
Patch-based methods are widely used in various topics of image processing, such as image restoration or image editing and synthesis. Patches capture local image geometry and structure and are much easier to model than whole images: in practice, patches are small enough to be represented by simple multivariate priors. An important question arising in all patch-based methods is the one of patch aggregation. For instance, in image restoration, restored patches are usually not compatible, in the sense that two overlap** restored patches do not necessarily yield the same values to their common pixels. A standard way to overcome this difficulty is to see the values provided by different patches at a given pixel as independent estimators of a true unknown value and to aggregate these estimators. This aggregation step usually boils down to a simple average, with uniform weights or with weights depending on the trust we have on these different estimators. In this paper, we propose a probabilistic framework aiming at a better understanding of this crucial and often neglected step. The key idea is to see the aggregation of two patches as a fusion between their models rather than a fusion of estimators. The proposed fusion operation is pretty intuitive and generalizes previous aggregation methods. It also yields a novel interpretation of the Expected Patch Log Likelihood (EPLL) proposed in Zoran and Weiss (in: 2011 IEEE international conference on computer vision (ICCV), 2011).
Similar content being viewed by others
Notes
For example, patches of size l of a 1D-signal \(x=(x_1, \ldots , x_n)\) are of the form \((x_i,\ldots ,x_{i+l})\), for \(i\in \llbracket 1,n-l \rrbracket \).
The precision matrix is the inverse of the covariance matrix.
References
Aguerrebere, C., Almansa, A., Delon, J., Gousseau, Y., Musé, P.: A bayesian hyperprior approach for joint image denoising and interpolation, with an application to hdr imaging. IEEE Trans. Comput. Imaging 3(4), 633–646 (2017)
Aharon, M., Elad, M., Bruckstein, A.: \(K\)-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Buades, A., Coll, B., Morel, J.-M.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2005)
Carrera, D., Boracchi, G., Foi, A., Wohlberg, B.: Sparse overcomplete denoising: aggregation versus global optimization. IEEE Signal Process. Lett. 24(10), 1468–1472 (2017)
Cho, T.S., Butman, M., Avidan, S., Freeman, W.T.: The patch transform and its applications to image editing. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE, pp. 1–8 (2008)
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)
Danielyan, A., Katkovnik, V., Egiazarian, K.: BM3D frames and variational image deblurring. IEEE Trans. Image Process. 21(4), 1715–1728 (2012)
Deledalle, C.-A., Duval, V., Salmon, J.: Non-local methods with shape-adaptive patches (NLM-SAP). J. Math. Imaging Vis. 43(2), 103–120 (2012)
Dengwen, Z., **aoliu, S.: Image denoising using weighted averaging. In: WRI International Conference on Communications and Mobile Computing, 2009. CMC’09. IEEE, vol. 1, pp. 400–403 (2009)
Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, ACM, pp. 341–346 (2001)
Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: Computer Vision, 1999. IEEE, vol. 2, pp. 1033–1038 (1999)
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)
Elad, M., Milanfar, P.: Style transfer via texture synthesis. IEEE Trans. Image Process. 26(5), 2338–2351 (2017)
Feng, J., Song, L., Huo, X., Yang, X., Zhang, W.: An optimized pixel-wise weighting approach for patch-based image denoising. IEEE Signal Process. Lett. 22(1), 115–119 (2015)
Frigo, O., Sabater, N., Delon, J., Hellier, P.: Split and match: example-based adaptive patch sampling for unsupervised style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 553–561 (2016)
Guleryuz, O.G.: Weighted averaging for denoising with overcomplete dictionaries. IEEE Trans. Image Process. 16(12), 3020–3034 (2007)
HaCohen, Y., Shechtman, E., Goldman, D.B., Lischinski, D.: Non-rigid dense correspondence with applications for image enhancement. In: ACM Transactions on Graphics (TOG), ACM, vol. 30, pp. 70 (2011)
Hoeting, J.A., Madigan, D., Raftery, A.E., Volinsky, C.T.: Bayesian model averaging: a tutorial. Stat. Sci. 382–401 (1999)
Houdard, A., Bouveyron, C., Delon, J.: High-dimensional mixture models for unsupervised image denoising (HDMI). SIAM J. Imaging Sci. 11(4), 2815–2846 (2018)
Kervrann, C.: PEWA: Patch-based exponentially weighted aggregation for image denoising. In: Advances in Neural Information Processing Systems, pp. 2150–2158 (2014)
Kervrann, C., Boulanger, J.: Optimal spatial adaptation for patch-based image denoising. IEEE Trans. Image Process. 15(10), 2866–2878 (2006)
Kwatra, V., Essa, I., Bobick, A., Kwatra, N.: Texture optimization for example-based synthesis. ACM Trans. Gr. (ToG) 24(3), 795–802 (2005)
Lebrun, M., Buades, A., Morel, J.-M.: A nonlocal bayesian image denoising algorithm. SIAM J. Imaging Sci. 6(3), 1665–1688 (2013)
Lebrun, M., Colom, M., Buades, A., Morel, J.-M.: Secrets of image denoising cuisine. Acta Numer. 21, 475–576 (2012)
Newson, A., Almansa, A., Fradet, M., Gousseau, Y., Pérez, P.: Video inpainting of complex scenes. SIAM J. Imaging Sci. 7(4), 1993–2019 (2014)
Paulino, I.F.R.: PACO: Signal restoration via patch consensus. ar**v preprint ar**v:1808.06942 (2018)
Pierazzo, N., Morel, J.-M., Facciolo, G.: Multi-scale DCT denoising. Image Process. On Line 7, 288–308 (2017)
Raad, L., Desolneux, A., Morel, J.-M.: A conditional multiscale locally gaussian texture synthesis algorithm. J. Math. Imaging Vis. 56(2), 260–279 (2016)
Romano, Y., Elad, M.: Boosting of image denoising algorithms. SIAM J. Imaging Sci. 8(2), 1187–1219 (2015)
Roth, S., Lempitsky, V., Rother, C.: Discrete-continuous optimization for optical flow estimation. In: Statistical and Geometrical Approaches to Visual Motion Analysis, Springer, pp. 1–22 (2009)
Salmon, J., Strozecki, Y.: From patches to pixels in non-local methods: Weighted-average reprojection. In: 2010 17th IEEE International Conference on Image Processing (ICIP). IEEE, pp. 1929–1932 (2010)
Sezer, O.G., Altunbasak, Y.: Weighted average denoising with sparse orthonormal transforms. In: 2009 16th IEEE International Conference on Image Processing (ICIP), IEEE, pp. 3849–3852 (2009)
Tabti, S., Deledalle, C-A., Denis, L., Tupin, F.: Modeling the distribution of patches with shift-invariance: application to SAR image restoration. In: 2014 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 96–100 (2014)
Talebi, H., Zhu, X., Milanfar, P.: How to SAIF-ly boost denoising performance. IEEE Trans. Image Process. 22(4), 1470–1485 (2013)
Teodoro, A.M., Almeida, M.S.C., Figueiredo, M.A.T.: Single-frame image denoising and inpainting using gaussian mixtures. In: ICPRAM (2), pp. 283–288 (2015)
Van De Ville, D., Kocher, M.: SURE-based non-local means. IEEE Signal Process. Lett. 16(11), 973–976 (2009)
Wang, Y.-Q., Morel, J.-M.: SURE guided Gaussian mixture image denoising. SIAM J. Imaging Sci. 6(2), 999–1034 (2013)
Guoshen, Y., Sapiro, G., Mallat, S.: Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity. IEEE Trans. Image Process. 21(5), 2481–2499 (2012)
Zontak, M., Mosseri, I., Irani, M.: Separating signal from noise using patch recurrence across scales. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1195–1202 (2013)
Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: 2011 IEEE International Conference on Computer Vision (ICCV), IEEE, pp. 479–486 (2011)
Acknowledgements
We would like to thank Arthur Leclaire and Pablo Arias for their insightful comments. We also would like to thank the three reviewers of this paper, whose comments and remarks have greatly helped to improve this work. This work has been partially funded by the French Research Agency (ANR) under Grant No ANR-14-CE27-001 (MIRIAM).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Saint-Dizier, A., Delon, J. & Bouveyron, C. A Unified View on Patch Aggregation. J Math Imaging Vis 62, 149–168 (2020). https://doi.org/10.1007/s10851-019-00921-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10851-019-00921-z