Chen Chen — Publications

C. Chen, “Multihypothesis Prediction for Compressed Sensing and Super-Resolution of Images,” Master's thesis, Mississippi State University, 2012. [BibTeX]
  • Abstract:
    A process for the use of multihypothesis prediction in the reconstruction of images is proposed for use in both compressed-sensing reconstruction as well as single-image super-resolution. Specifically, for compressed-sensing reconstruction of a single still image, multiple predictions for an image block are drawn from spatially surrounding blocks within an initial non-predicted reconstruction. The predictions are used to generate a residual in the domain of the compressed-sensing random projections. This residual being typically more compressible than the original signal leads to improved compressed-sensing reconstruction quality. To appropriately weight the hypothesis predictions, a Tikhonov regularization to an ill-posed least-squares optimization is proposed. An extension of this framework is applied to the compressed-sensing reconstruction of hyperspectral imagery is also studied. Finally, the multihypothesis paradigm is employed for single-image super-resolution wherein each patch of a low-resolution image is represented as a linear combination of spatially surrounding hypothesis patches.

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