  Motivated with the concept of transform learning and the utility of rational
wavelet transform in audio and speech processing, this paper proposes Rational
Wavelet Transform Learning in Statistical sense (RWLS) for natural images. The
proposed RWLS design is carried out via lifting framework and is shown to have
a closed form solution. The efficacy of the learned transform is demonstrated
in the application of compressed sensing (CS) based reconstruction. The learned
RWLS is observed to perform better than the existing standard dyadic wavelet
transforms.
