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Foveated Nonlocal Self-Similarity

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Foveated Nonlocal Self-Similarity. / Foi, Alessandro; Boracchi, Giacomo.

In: International Journal of Computer Vision, Vol. 120, No. 1, 2016, p. 78–110.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Foi, A & Boracchi, G 2016, 'Foveated Nonlocal Self-Similarity', International Journal of Computer Vision, vol. 120, no. 1, pp. 78–110. https://doi.org/10.1007/s11263-016-0898-1

APA

Foi, A., & Boracchi, G. (2016). Foveated Nonlocal Self-Similarity. International Journal of Computer Vision, 120(1), 78–110. https://doi.org/10.1007/s11263-016-0898-1

Vancouver

Foi A, Boracchi G. Foveated Nonlocal Self-Similarity. International Journal of Computer Vision. 2016;120(1):78–110. https://doi.org/10.1007/s11263-016-0898-1

Author

Foi, Alessandro ; Boracchi, Giacomo. / Foveated Nonlocal Self-Similarity. In: International Journal of Computer Vision. 2016 ; Vol. 120, No. 1. pp. 78–110.

Bibtex - Download

@article{f56eefa5d70c4b7eb6ce0891c06e2d4f,
title = "Foveated Nonlocal Self-Similarity",
abstract = "When we gaze a scene, our visual acuity is maximal at the fixation point (imaged by the fovea, the central part of the retina) and decreases rapidly towards the periphery of the visual field. This phenomenon is known as foveation. We investigate the role of foveation in nonlocal image filtering, installing a different form of self-similarity: the foveated self-similarity. We consider the image denoising problem as a simple means of assessing the effectiveness of descriptive models for natural images and we show that, in nonlocal image filtering, the foveated self-similarity is far more effective than the conventional windowed self-similarity. To facilitate the use of foveation in nonlocal imaging algorithms, we develop a general framework for designing foveation operators for patches by means of spatially variant blur. Within this framework, we construct several parametrized families of operators, including anisotropic ones. Strikingly, the foveation operators enabling the best denoising performance are the radial ones, in complete agreement with the orientation preference of the human visual system.",
author = "Alessandro Foi and Giacomo Boracchi",
note = "EXT={"}Boracchi, Giacomo{"}",
year = "2016",
doi = "10.1007/s11263-016-0898-1",
language = "English",
volume = "120",
pages = "78–110",
journal = "International Journal of Computer Vision",
issn = "0920-5691",
publisher = "Springer Verlag",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Foveated Nonlocal Self-Similarity

AU - Foi, Alessandro

AU - Boracchi, Giacomo

N1 - EXT="Boracchi, Giacomo"

PY - 2016

Y1 - 2016

N2 - When we gaze a scene, our visual acuity is maximal at the fixation point (imaged by the fovea, the central part of the retina) and decreases rapidly towards the periphery of the visual field. This phenomenon is known as foveation. We investigate the role of foveation in nonlocal image filtering, installing a different form of self-similarity: the foveated self-similarity. We consider the image denoising problem as a simple means of assessing the effectiveness of descriptive models for natural images and we show that, in nonlocal image filtering, the foveated self-similarity is far more effective than the conventional windowed self-similarity. To facilitate the use of foveation in nonlocal imaging algorithms, we develop a general framework for designing foveation operators for patches by means of spatially variant blur. Within this framework, we construct several parametrized families of operators, including anisotropic ones. Strikingly, the foveation operators enabling the best denoising performance are the radial ones, in complete agreement with the orientation preference of the human visual system.

AB - When we gaze a scene, our visual acuity is maximal at the fixation point (imaged by the fovea, the central part of the retina) and decreases rapidly towards the periphery of the visual field. This phenomenon is known as foveation. We investigate the role of foveation in nonlocal image filtering, installing a different form of self-similarity: the foveated self-similarity. We consider the image denoising problem as a simple means of assessing the effectiveness of descriptive models for natural images and we show that, in nonlocal image filtering, the foveated self-similarity is far more effective than the conventional windowed self-similarity. To facilitate the use of foveation in nonlocal imaging algorithms, we develop a general framework for designing foveation operators for patches by means of spatially variant blur. Within this framework, we construct several parametrized families of operators, including anisotropic ones. Strikingly, the foveation operators enabling the best denoising performance are the radial ones, in complete agreement with the orientation preference of the human visual system.

U2 - 10.1007/s11263-016-0898-1

DO - 10.1007/s11263-016-0898-1

M3 - Article

VL - 120

SP - 78

EP - 110

JO - International Journal of Computer Vision

JF - International Journal of Computer Vision

SN - 0920-5691

IS - 1

ER -