Conference Paper

S. Mun and J. E. Fowler, “Block Compressed Sensing of Images Using Directional Transforms,” in Proceedings of the International Conference on Image Processing, Cairo, Egypt, November 2009, pp. 3021-3024.

• Abstract:
  Block-based random image sampling is coupled with a projection-driven 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.
• Text:
• Source Code: See the BCS-SPL website.

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