Some interesting IEEE articles arrived in my mailbox this week. Computing in Science and Engineering has "MCALab: Reproducible Research in Signal and Image Decomposition and Inpainting," by Fadili, Starck, Elad, and Donoho. MCA means morphological component analysis, which is a new term for me. MCALab is an open-source library of MATLAB routines for signal and image decomposition and inpainting.
This month's Signal Processing Magazine has a special section on astronomy and cosmology packed with image processing theory and applications, including:
- "Synthetic Aperture Radio Telescopes," by Levanda and Leshem
- "Calibration Challenges for Future Radio Telescopes," by Wijnholds, van der Tol, Nijboer, and van der Veen
- "Bayesian Source Separation for Cosmology," by Kuruoglu
- "Precision Cosmology with the Cosmic Microwave Background," by Cardoso
- "Cosmic Microwave Background Images," by Herranz and Vielva
- "Light on Dark Matter with Weak Gravitational Lensing," by Pires, Starck, and Réfrégier.
- "Multidimensional Image Reconstruction in Astronomy," by Kamalabadi
- "Image Reconstruction in Optical Interferometry," by Thiébaut and Giovannelli
- "Imaging with Linc-Nirvana," by Bertero, Boccacci, Desiderà, La Camera, Carbillet, and Lantéri
Finally, another article in Computing in Science and Engineering caught my eye: "Whip Until Solved," by Sullivan. This article describes a method for solving Sudoku puzzles that makes use of the MATLAB function dmperm. I confess that I can't think of a connection between Sudoku and image processing (take that as a challenge, dear reader), and I've never even solved a Sudoku puzzle. But I have written about dmperm, though, in my series on connected components labeling. And, coincidentally, MATLAB creator Cleve Moler wrote an article in the most recent MathWorks News & Notes about different way to use MATLAB to solve Sudoku puzzles.
I've got an item on my to-do list that says "Study Cleve's Sudoku code to see how it works." Not sure when I'll get the time to do that, though.
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