Characterization of Color Images with Multiscale Monogenic Maxima.

Can we build a feature-based analysis that fully characterizes images? The literature answers with edge-based reconstruction methods inspired by Marr's paradigm but limited to the greyscale case. This paper studies the color case. A new sparse representation is carried out with the monogenic concept and the Mallat-Zhong wavelet maxima method.

Vector extension of monogenic wavelets for geometric representation of color images.

Monogenic wavelets offer a geometric representation of grayscale images through an AM-FM model allowing invariance of coefficients to translations and rotations. The underlying concept of local phase includes a fine contour analysis into a coherent unified framework. Starting from a link with structure tensors, we propose a nontrivial extension of the monogenic framework to vector-valued signals to carry out a nonmarginal color monogenic wavelet transform.