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Foveated Nonlocal Self-Similarity

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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.

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Notes

  1. In our experiments we resort to symmetric padding.

  2. Because our foveation operators are always designed upon a given windowing kernel \(\mathbf {k}\), a more precise symbol would be \(\mathcal {F}_{\mathbf {k} } \). However, not to overload the notation, we omit the subscript \(\mathbf {k} \) and leave room to other decorations that are used in the later sections.

  3. These five requirements include the four requirements described in our preliminary conference publications (Foi and Boracchi 2012, 2013b, a), plus a non-negativity requirement, which had been always tacitly assumed but never mentioned explicitly.

  4. In particular, the recursive sequence \(\left\{ \varsigma _{n}\right\} _{n=0}^{+\infty }\) defined by

    $$\begin{aligned} \varsigma _{0}=\tfrac{1}{2\sqrt{\pi }}\sqrt{\tfrac{\mathbf {k}\left( 0\right) }{\mathbf {k}\left( u\right) }},\qquad \varsigma _{n+1}=\varsigma _{n}\left\| \bar{g}_{\varsigma _{n},u}^{\rho ,\vartheta }\right\| _{2} \sqrt{\tfrac{\mathbf {k}\left( 0\right) }{\mathbf {k}\left( u\right) }} \end{aligned}$$

    converges monotonically to the solution \(\overset{_{*}}{\varsigma } _{u}^{\rho ,\vartheta }\) of (49), with geometric rate for any \(\mathbf {k}\left( u\right) <\mathbf {k}\left( 0\right) \) (contraction mapping).

  5. Note that \(\bar{v}_{u_{i}}^{\rho ,\vartheta }\left( u_{j}\right) \) is nothing but the inner product between a Dirac patch at \(u_{i}\) and \(\mathcal { B}_{\rho ,\theta }\) applied to another Dirac patch at \(u_{j}\): \(\bar{v} _{u_{i}}^{\rho ,\vartheta }\left( u_{j}\right) =\left\langle \mathbf {\delta } _{u_{i}},\mathcal {B}_{\rho ,\theta }\left[ \mathbf {\delta }_{u_{j}}\right] \right\rangle \).

  6. http://www.cs.tut.fi/~foi/FoveatedNL

  7. The selected parameter range is general enough since on natural images the denoising performance of left-hand chiral operators (\(\rho \ge 1\), \(-\pi /2<\theta <0\)) is practically identical to those of right-hand chiral operators (\(\rho \ge 1\), \(0<\theta <\pi /2\)), as shown in Section Suppl.4.

  8. For Gaussian blur kernels, \(\mathcal {F}_{\rho ,0}=\mathcal {F}_{1/\rho ,\pi /2}\).

  9. Invertibility is feasible with self-map operators \(\mathcal {F}_{\rho ,\theta }^{\text {self}}\) provided that the inequality (50) is met with a strict lower bound. As a matter of fact, the greatest advantage of self-map operators is that they can be represented through \( \mathcal {B}_{\rho ,\theta }\) (46) as square matrices of size \( \left| U\right| \times \left| U\right| \).

  10. In fact, \(\mathcal {F}_{\rho ,\theta }[z,x_{1}](\bar{u})\) is proportional to the value in \(\bar{u}\) of the convolution between z and the blur kernel \( v_{\bar{u}}\), and this latter has a main axis oriented as \(\angle \bar{u} +\theta \)

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Acknowledgments

This work was supported by the Academy of Finland (Project No. 252547, Academy Research Fellow 2011-2016).

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Authors and Affiliations

  1. Department of Signal Processing, Tampere University of Technology, Tampere, Finland

    Alessandro Foi

  2. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy

    Giacomo Boracchi

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  1. Alessandro Foi
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  2. Giacomo Boracchi
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Correspondence to Alessandro Foi.

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Communicated by Stefan Roth.

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Foi, A., Boracchi, G. Foveated Nonlocal Self-Similarity. Int J Comput Vis 120, 78–110 (2016). https://doi.org/10.1007/s11263-016-0898-1

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