Compressive Sensing for Polyharmonic Subdivision Wavelets With Applications to Image Analysis

TL;DR

This paper demonstrates that applying Compressive Sensing with Polyharmonic Subdivision wavelets enables efficient image recovery from fewer samples, outperforming traditional methods in image analysis tasks.

Contribution

It introduces the use of Polyharmonic Subdivision wavelets within Compressive Sensing for improved image reconstruction with fewer samples.

Findings

Efficient image recovery with fewer samples using PHSD wavelets.

Comparison shows PHSD wavelets outperform Daubechies wavelets in experiments.

Successful application to Lena and astronomical images.

Abstract

We apply successfully the Compressive Sensing approach for Image Analysis using the new family of Polyharmonic Subdivision wavelets. We show that this approach provides a very efficient recovery of the images based on fewer samples than the traditional Shannon-Nyquist paradigm. We provide the results of experiments with PHSD wavelets and Daubechies wavelets, for the Lena image and astronomical images.