Wavelets perform well only at representing point singularities since they ignore the geometric properties of structures and do not exploit the regularity of edges. Therefore, wavelet-based compression, denoising, or structure extraction become computationally inefficient for geometric features with line and surface singularities. The curvelet transform is a multiscale directional transform that allows an almost optimal nonadaptive sparse representation of objects with edges.