2012Zhao Denoising

Citation

Zhao, Z. & Singer, A. Fourier-Bessel rotational invariant eigenimages J. Optical Soc. America A, 2012, 30, 871-877

Abstract

We present an efficient and accurate algorithm for principal c omponent analysis (PCA) of a large set of two- dimensional images, and, for each image, the set of its unifo rm rotations in the plane and its reflection. The algorithm starts by expanding each image, originally given on a Cartesian grid, in the Fourier-Bessel basis for the disk. Because the images are essentially bandlimited in the Fourier domain, we use a sampling criterion to truncate the Fourier-Bessel expansion such that the maximu m amount of information is preserved without the effect of aliasing. The constructed covariance matrix is inv ariant to rotation and reflection and has a special block diagonal structure. PCA is efficiently done for each blo ck separately. This Fourier-Bessel based PCA detects more meaningful eigenimages and has improved denoi sing capability compared to traditional PCA for a finite number of noisy images.

Keywords

Denoising, PCA, rotational invariant basis

Links

http://arxiv.org/abs/1211.1968