Multiscale Block Compressed Sensing with Smoothed Projected Landweber Reconstruction

J. E. Fowler and S. Mun, & E. W. Tramel

Multiscale Block Compressed Sensing with Smoothed Projected Landweber Reconstruction
Reconstructions of the \(512 \times 512\) Lenna image (shown in detail) for a subrate of \(S = 0.1\).

Abstract

A multiscale variant of the block compressed sensing with smoothed projected Landweber reconstruction algorithm is proposed for the compressed sensing of images. In essence, block-based compressed-sensing sampling is deployed independently within each subband of each decomposition level of a wavelet transform of an image. The corresponding multiscale reconstruction interleaves Landweber steps on the individual blocks with a smoothing filter in the spatial domain of the image as well as thresholding within a sparsity transform. Experimental results reveal that the proposed multiscale reconstruction preserves the fast computation associated with block-based compressed sensing while rivaling the reconstruction quality of a popular total-variation algorithm known for both its high-quality reconstruction as well as its exceedingly large computational cost.

BibTeX

inproceedings{fmt2011,
    Address = {Barcelona, Spain},
    Author = {James E. Fowler and Sungkwang Mun and Eric W. Tramel},
    Booktitle = {Proc. European Signal Processing Conf. (EUSIPCO)},
    Month = {August},
    Pages = {564--568},
    Title = {Multiscale Block Compressed Sensing with Smoothed Projected Landweber Reconstruction},
    Year = {2011}}