Fast expansion into harmonics on the disk: a steerable basis with fast radial convolutions
This provides a computationally efficient method for image processing tasks on disk-shaped domains, such as in astronomy or medical imaging, though it is incremental as it builds on existing Fourier-Bessel basis concepts.
The authors tackled the problem of efficiently expanding images on a disk into Fourier-Bessel harmonics, resulting in the Fast Disk Harmonics Transform (FDHT) that runs in O(L^2 log L) operations and enables fast radial convolutions via diagonal transforms.
We present a fast and numerically accurate method for expanding digitized $L \times L$ images representing functions on $[-1,1]^2$ supported on the disk $\{x \in \mathbb{R}^2 : |x|<1\}$ in the harmonics (Dirichlet Laplacian eigenfunctions) on the disk. Our method, which we refer to as the Fast Disk Harmonics Transform (FDHT), runs in $O(L^2 \log L)$ 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.