  The empirical wavelet transform is a data-driven time-scale representation
consisting of adaptive filters. Its robustness to data has made it the subject
of intense developments and an increasing number of applications in the last
decade. However, it has been mostly studied theoretically for signals so far
and its extension to images is limited to a particular mother wavelet. This
work presents a general framework for multidimensional empirical wavelet
transform from any mother wavelet. In addition, it provides conditions to build
wavelet frames for both continuous and discrete transforms.
