Complex wavelet transform

It is a two-dimensional wavelet transform which provides multiresolution, sparse representation, and useful characterization of the structure of an image.

In the area of computer vision, by exploiting the concept of visual contexts, one can quickly focus on candidate regions, where objects of interest may be found, and then compute additional features through the CWT for those regions only.

These additional features, while not necessary for global regions, are useful in accurate detection and recognition of smaller objects.

Similarly, the CWT may be applied to detect the activated voxels of cortex and additionally the temporal independent component analysis (tICA) may be utilized to extract the underlying independent sources whose number is determined by Bayesian information criterion [1][permanent dead link‍].

If the filters used in one are specifically designed different from those in the other it is possible for one DWT to produce the real coefficients and the other the imaginary.