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. Author manuscript; available in PMC: 2017 Oct 27.
Published in final edited form as: SIAM J Imaging Sci. 2017 Apr 13;10(2):508–534. doi: 10.1137/16M1085334

Algorithm 3. PSWFs-based steerable PCA.

  1. Required: PSWFs expansion coefficients of M images {a^N,nm}m=0M−1 for (N, n) ∈ ΩT and a bandlimit c.

  2. Precomputation: Evaluate the PSWFs ψN,nc(kL), kL∈D and their eigenvalues αN,nc for (N, n) ∈ ΩT according to [31].

  3. Compute the expansion coefficients of the mean image for μ^0,n=1M∑m=0M−1a^0,nm for n = 0, …, n0 where n0 is the largest index n such that (0, n) ∈ ΩT.

  4. Update a^0,nm←a^0,nm−μ^0,n.

  5. Compute the eigenvalues λ̂1, …, λ̂|ΩT| and eigenvectors ĝ1, …, ĝ|ΩT| of the matrix C (from (30)) by diagonalizing each of its blocks separately.

  6. Compute the sampled basis functions gℓ(kL)=∑n=0nℓg^Nℓ,nℓψNℓ,nℓ(kL), where nℓ stands for the largest index n such that (Nℓ, n) ∈ ΩT, and g^Nℓ,nℓ are the entries of the eigenvector ĝℓ corresponding to the pair (Nℓ, n).

  7. Compute the coefficients of Im in the steerable-PCA basis by cℓ,m=∑n=0nℓa^Nℓ,nm(g^Nℓ,nℓ)∗.