Statistical learning of rational wavelet transform for natural images
This work addresses image processing challenges by improving reconstruction quality, but it is incremental as it builds on existing transform learning and wavelet methods.
The paper tackled the problem of designing a rational wavelet transform for natural images using a statistical learning approach, achieving better performance than standard dyadic wavelet transforms in compressed sensing reconstruction.
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.