Blind image restoration is the process of estimating both the true image and the blur from the degraded image, using only partial information about degradation sources and the imaging system. Our main interest concerns optical image enhancement, where the degradation often involves a convolution process. We provide a method to incorporate truncated eigenvalue and total variation regularization into a nonlinear recursive inverse filter (RIF) blind deconvolution scheme first proposed by Kundar, and by Kundur and Hatzinakos. Tests are reported on simulated and optical imaging problems.
In array processing, one technique for cancelling interference in the presence of colored noise is the ULLV decomposition of a pair of matrices. The factorization is stable and accurate, and is easy to update when a row is added to either one of the two matrices. In earlier work, we made the assumptions that both matrices must have at least as many rows as columns and that the matrix representing interference must have full column rank. In this paper, we relax the latter restriction and allow the interference matrix to be rank deficient. We present an algorithm for computing the decomposition and two techniques for updating it. The details on updating are quite different from those in the full rank case.
This paper describes a parallel implementation of a sequence alignment algorithm for biomolecular sequence analysis. It uses multiple threaded programming for the most time consuming functions and works in X Window based interactive systems. Its sequence alignment operations include Pairwise Alignment, Star Alignment, Phylogeny Reconstruction and Generalized Tree Alignment. Both of fast and optimal modes are provided. The algorithms for Phylogeny Reconstruction, Generalized Tree Alignment, and Tree Alignment are based on heuristic stepwise addition and internal node sequence alignment induction methods.PTAR can be used for DNA, RNA, and PRO-TEIN sequence analysis. In general, the system can carry out the alignments for any sequences composed of characters a-z and A-Z.
This paper presents a hardware structure for a parallel implementation of an efficient least squares algorithm for linear prediction problem presented in [11]. Experimented results on a Warp systolic multiprocessor computer show the actual speed up of the parallel implementation.
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