Compressed Sensing of Multiview Images using Disparity Compensation

M. Trocan, T. Maugey, E. W. Tramel, J. E. Fowler, & B. Pesquet-Popescu

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Astract

Compressed sensing is applied to multiview image sets and inter-image disparity compensation is incorporated into image reconstruction in order to take advantage of the high degree of inter-image correlation common to multiview scenarios. Instead of recovering images in the set independently from one another, two neighboring images are used to calculate a prediction of a target image, and the difference between the original measurements and the compressed-sensing projection of the prediction is then reconstructed as a residual and added back to the prediction in an iterated fashion. The proposed method shows large gains in performance over straightforward, independent compressed-sensing recovery. Additionally, projection and recovery are block-based to significantly reduce computation time.

BibTeX

    inproceedings{tmt2010,
    Address = {Hong Kong},
    Author = {Maria Trocan and Thomas Maugey and Eric W. Tramel and James E. Fowler and B{\'e}atrice Pesquet-Popescu},
    Booktitle = {Proc. of the {IEEE} Int. Conf. on Image Processing (ICIP)},
    Month = sep,
    Pages = {3345--3348},
    Title = {Compressed Sensing of Multiview Images using Disparity Compensation},
    Year = 2010}