James E. Fowler — Publications
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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