High-resolution images are essential in many important areas of science, engineering, law enforcement, and medicine. The need to extract meaningful information from degraded images is vital in many areas such as aero-optics imaging, surveillance photography, confocal microscopy, and medical imaging. Sources of image degradation vary among application areas, but include atmospheric turbulence, turbidity in a fluid medium, motion blur, and numerous other effects. Image processing tasks such as image enhancement, reconstruction, and restoration are computationally intensive; the underlying physical models require the solution to data-massive and often ill-posed inverse problems where the solution may not depend continuously on the data. All such problems require significant research into the algorithm- architecture-application interaction and can profit substantially from the availability of high-performance software systems. Iterative solvers that exploit both the structure of the problem, and the parallel computing platform, appear to be the most viable candidates for diverse, large scale image enhancement problems. This research project includes the following technical topics: a) New iterative algorithms with effective regularization strategies for blind deconvolution image restoration using adaptive filtering and multichannel phase diversity methods. b) Acceleration of the iterative algorithms utilizing Krylov subspace based multi-level preconditioning. c) New algorithms which partially mitigate the ill-posed nature of the image restoration problem by integrating numerical and symbolic computational methods. d) Development of a high performance software package, entitled "Parallel Image Processing Environment (pipe})", which utilizes the image processing algorithms developed in the course of this research.
