The discrete curvelet transform decomposes an image into a set of fundamental components that are distinguished by direction and size and a low-frequency representation. The curvelet representation of a natural image is approximately sparse; thus, it is useful for compressed sensing.
Utilizing the structure of a redundant dictionary comprised of wavelets and curvelets with compressed sensing
minute read
by Journal of Electronic Imaging | December 6, 2022
