M A Neifeld | PubMed Health

PubMed2001

Soft-decision array decoding for volume holographic memory systems.

We study the use of soft-decision array decoding in a volume holographic memory (VHM) system that is corrupted by interpixel interference (IPI) and detector noise. Soft-decision methods can unify equalization and error decoding. A highly parallel array decoder is presented in the context of two-dimensional low-pass channel mitigation and error correction. The new decoding algorithm is motivated by iterative turbo-decoding methods and is capable of incorporating a priori knowledge of the corrupting IPI channel during decoding. The resulting joint detection decoding algorithm is shown to offer VHM capacity and density performance superior to that of hard-decision n = 255 Reed-Solomon codes in concatenation with a Wiener filter.

Journal Article↗

PubMed2000

Image restoration with the Viterbi algorithm.

The Viterbi algorithm (VA) is known to given an optimal solution to the problem of estimating one-dimensional sequences of discrete-valued pixels corrupted by finite-support blur and memoryless noise. A row-by-row estimation along with decision feedback and vector quantization is used to reduce the computational complexity of the VA and allow the estimation of two-dimensional images. This reduced-complexity VA (RCVA) is shown to produce near-optimal estimation of random binary images. In addition, simulated restorations of gray-scale images show the RCVA estimates to be an improvement over the estimates obtained by the conventional Wiener filter (WF). Unlike the WF, the RCVA is capable of superresolution and is adaptable for use in restoring data from signal-dependent Poisson noise corruption. Experimental restorations of random binary data gathered from an optical imaging system support the simulations and show that the RCVA estimate has fewer than one third of the errors of the WF estimate.

Algorithms↗