Steerable pyramid

Steerable pyramid toolbox. The Steerable Pyramid is a linear multi-scale, multi-orientation image decomposition that provides a useful front-end for image-processing and computer vision applications. We developed this representation in 1990, in order to overcome the limitations of orthogonal separable wavelet decompositions that were then becoming popular for image processing (specifically, those representations are heavily aliased, and do not represent oblique orientations well). Once the orthogonality constraint is dropped, it makes sense to completely reconsider the filter design problem (as opposed to just re-using orthogonal wavelet filters in a redundant representation, as is done in cycle-spinning or undecimated wavelet transforms!). Detailed information may be found in the references listed below.


References in zbMATH (referenced in 79 articles )

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  1. Atreas, N.; Karantzas, N.; Papadakis, M.; Stavropoulos, T.: On the design of multi-dimensional compactly supported Parseval framelets with directional characteristics (2019)
  2. Lessig, Christian: Divergence free polar wavelets for the analysis and representation of fluid flows (2019)
  3. Püspöki, Zsuzsanna; Fageot, Julien; Amini, Arash; Ward, John Paul; Unser, Michael: Angular accuracy of steerable feature detectors (2019)
  4. Sánchez Giraldo, Luis Gonzalo; Schwartz, Odelia: Integrating flexible normalization into midlevel representations of deep convolutional neural networks (2019)
  5. Arias-Castro, Ery; Bubeck, Sébastien; Lugosi, Gábor; Verzelen, Nicolas: Detecting Markov random fields hidden in white noise (2018)
  6. Bruna, Joan; Mallat, Stéphane: Multiscale sparse microcanonical models (2018)
  7. Galerne, B.; Leclaire, A.; Rabin, J.: A texture synthesis model based on semi-discrete optimal transport in patch space (2018)
  8. Nelson, J. D. B.; Gibberd, A. J.; Nafornita, C.; Kingsbury, N.: The locally stationary dual-tree complex wavelet model (2018)
  9. Jouini, Mohamed Soufiane; Keskes, Noomane: Numerical estimation of rock properties and textural facies classification of core samples using X-ray computed tomography images (2017)
  10. Marchant, Ross; Jackway, Paul: A sinusoidal image model derived from the circular harmonic vector (2017)
  11. Mirkes, Evgeny M.; Gorban, Alexander N.; Levesley, Jeremy; Elkington, Peter A. S.; Whetton, James A.: Pseudo-outcrop visualization of borehole images and core scans (2017)
  12. Chatterjee, Snehamoy; Mustapha, Hussein; Dimitrakopoulos, Roussos: Fast wavelet-based stochastic simulation using training images (2016)
  13. Raad, Lara; Desolneux, Agnès; Morel, Jean-Michel: A conditional multiscale locally Gaussian texture synthesis algorithm (2016)
  14. Tartavel, Guillaume; Peyré, Gabriel; Gousseau, Yann: Wasserstein loss for image synthesis and restoration (2016)
  15. Tygert, Mark; Bruna, Joan; Chintala, Soumith; LeCun, Yann; Piantino, Serkan; Szlam, Arthur: A mathematical motivation for complex-valued convolutional networks (2016)
  16. Wang, Yi-Qing: A note on the size of denoising neural networks (2016)
  17. Alvarez, Luis; Gousseau, Yann; Morel, Jean-Michel; Salgado, Agustín: Exploring the space of abstract textures by principles and random sampling (2015) ioport
  18. Chamorro-Martínez, Jesús; Martínez-Jiménez, Pedro Manuel; Soto-Hidalgo, José Manuel; Prados-Suárez, Belén: Fuzzy sets on 2D spaces for fineness representation (2015)
  19. Han, Zhi; Xu, Zongben; Zhu, Song-Chun: Video primal sketch: a unified middle-level representation for video (2015)
  20. Tartavel, Guillaume; Gousseau, Yann; Peyré, Gabriel: Variational texture synthesis with sparsity and spectrum constraints (2015)

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