The steerable graph Laplacian and its application to filtering image datasets Boris Landa and Yoel Shkolnisky August 9, 2018 Boris Landa Department of Applied Mathematics, School of Mathematical Sciences Tel-Aviv University
[email protected] Yoel Shkolnisky Department of Applied Mathematics, School of Mathematical Sciences Tel-Aviv University
[email protected] Please address manuscript correspondence to Boris Landa,
[email protected], (972) 549427603. arXiv:1802.01894v2 [cs.CV] 7 Aug 2018 1 Abstract In recent years, improvements in various image acquisition techniques gave rise to the need for adaptive processing methods, aimed particularly for large datasets corrupted by noise and deformations. In this work, we consider datasets of images sampled from a low-dimensional manifold (i.e. an image- valued manifold), where the images can assume arbitrary planar rotations. To derive an adaptive and rotation-invariant framework for processing such datasets, we introduce a graph Laplacian (GL)-like operator over the dataset, termed steerable graph Laplacian. Essentially, the steerable GL extends the standard GL by accounting for all (infinitely-many) planar rotations of all images. As it turns out, similarly to the standard GL, a properly normalized steerable GL converges to the Laplace-Beltrami operator on the low-dimensional manifold. However, the steerable GL admits an improved convergence rate compared to the GL, where the improved convergence behaves as if the intrinsic dimension of the underlying manifold is lower by one. Moreover, it is shown that the steerable GL admits eigenfunctions of the form of Fourier modes (along the orbits of the images’ rotations) multiplied by eigenvectors of certain matrices, which can be computed efficiently by the FFT.