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- Charles L. Byrne
- IEEE Trans. Image Processing
- 1993

The related problems of minimizing the functionals F(x)=alphaKL(y,Px)+(1-alpha)KL(p,x) and G(x)=alphaKL(Px,y)+(1-alpha)KL(x,p), respectively, over the set of vectors x=/>0 are considered. KL(a, b) is the cross-entropy (or Kullback-Leibler) distance between two nonnegative vectors a and b. Iterative algorithms for minimizing both functionals using the method… (More)

- James H. McClellan, Ronald Schafer, +14 authors E. V. Pasternak
- 2009

DOWNLOAD http://bit.ly/1JYJ2vI Signal Processing First For introductory courses (sophomore/junior) in Digital Signal Processing and Signals and Systems. Text is useful as a self-teaching tool for anyone eager to discover more about DSP applications, multi-media signals, and MATLAB. This text is derived from DSP First: A Multimedia Approach, published in… (More)

- Charles L. Byrne, Francis Sullivan, +5 authors Steve Glick
- 2007

- Charles L. Byrne
- IEEE Trans. Med. Imaging
- 2001

The maximum a posteriori (MAP) Bayesian iterative algorithm using priors that are gamma distributed, due to Lange, Bahn and Little, is extended to include parameter choices that fall outside the gamma distribution model. Special cases of the resulting iterative method include the expectation maximization maximum likelihood (EMML) method based on the Poisson… (More)

- Lee K. Jones, Charles L. Byrne
- IEEE Trans. Information Theory
- 1990

- Charles L. Byrne
- IEEE Trans. Image Processing
- 1997

It has been shown that convergence to a solution can be significantly accelerated for a number of iterative image reconstruction algorithms, including simultaneous Cimmino-type algorithms, the "expectation maximization" method for maximizing likelihood (EMML) and the simultaneous multiplicative algebraic reconstruction technique (SMART), through the use of… (More)

- Richard M. Leahy, Charles L. Byrne
- IEEE Trans. Med. Imaging
- 2000

- Charles L. Byrne
- 2013

The forward-backward splitting (FBS) algorithm is a quite general iterative method that includes, as particular cases, the projected gradient descent algorithm for constrained minimization, the CQ algorithm for the split feasibility problem, the projected Landweber algorithm for constrained least squares, and the simultaneous orthogonal projection algorithm… (More)

The convex feasibility problem (CFP) is to find a member of the intersection of finitely many closed convex sets in Euclidean space. When the intersection is empty, one can minimize a proximity function to obtain an approximate solution to the problem. The split feasibility problem (SFP) and the split equality problem (SEP) are generalizations of the CFP.… (More)

- Charles L. Byrne
- 2006