Block Compressed Sensing of Images Using Directional Transforms

Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing.

• Type: Conference
• Year: 2010
• Where: DCC
• Authors: Sungkwang Mun, James E. Fowler
• Keywords: Block-based Random Image, Compressed-sensing Recovery, Computer Graphics, DCC 2010, Requisite Sparsity Constraint