April 15, 2011  Rachel Ward, CIMS

Title: A symbol-based algorithm for decoding noisy Bar Codes
  

  Bar code reconstruction involves recovering a clean signal from an observed signal that is blurry and corrupted by additive noise. The precise form of the blur kernel is unknown, making reconstruction harder than standard deblurring. On the other hand, the set of valid bar codes is very small relative to the set of all binary sequences, and this additional information makes reconstruction feasible. In this talk we show how bar code reconstruction can be re-cast as a sparse-recovery problem, and we develop a fast symbology-based reconstruction algorithm. This is joint work with Fadil Santosa and Mark Iwen.