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
This is joint work with Fadil Santosa and Mark Iwen.