mlsanityData to 7 Oct 2026

Fast expansion into harmonics on the disk: a steerable basis with fast radial convolutions

arXiv 2207.13674 ↗First posted 27 Jul 2022math.NA cs.LG cs.NAcited by 1

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Abstract (arXiv)

We present a fast and numerically accurate method for expanding digitized L × L images representing functions on [-1,1]2 supported on the disk x ∈ ℝ2 : |x|<1 in the harmonics (Dirichlet Laplacian eigenfunctions) on the disk. Our method, which we refer to as the Fast Disk Harmonics Transform (FDHT), runs in O(L2 log L) operations. This basis is also known as the Fourier-Bessel basis, and it has several computational advantages: it is orthogonal, ordered by frequency, and steerable in the sense that images expanded in the basis can be rotated by applying a diagonal transform to the coefficients. Moreover, we show that convolution with radial functions can also be efficiently computed by applying a diagonal transform to the coefficients.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 49 references that are in the map. First posted on arXiv 27 Jul 2022.

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8 of 49 references.

References · 8

  1. 2016Steerable CNNscited by 298
  2. 2017Learning Steerable Filters for Rotation Equivariant CNNscited by 193
  3. 2013Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EMcited by 17
  4. 2018RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networkscited by 17
  5. 2014Fast Steerable Principal Component Analysiscited by 12
  6. 2021cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTscited by 11
  7. 2016Denoising and Covariance Estimation of Single Particle Cryo-EM Imagescited by 10
  8. 2016Steerable Principal Components for Space-Frequency Localized Imagescited by 3

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