Stephen J. Garnier

TL;DR: This paper demonstrates how adaptive wavelet basis can be used to locate defects in woven fabrics.

TL;DR: The mathematics of MFA are shown to provide a powerful and general tool for deriving optimization algorithms.

TL;DR: A novel technique for Magnetic Resonance Image (MRI) restoration, using a physical model (spin equation) and corresponding basis images, and a novel method of global optimization necessary for the nonlinear technique is introduced.

Abstract: Our intent is to obtain images which most clearly differentiate soft tissue types in Magnetic Resonance Image data. We model the three unknown intrinsic parameter images and the data images as Markov random fields and compare maximum likelihood restorations with two maximum a posteriori (MAP) restorations. The mathematical model of the imaging process is strongly nonlinear in the region of interest, but does not appear to introduce local minima in the resulting constrained multidimensional optimization procedure. The application of non- quadratic prior probabilities however does require global optimization. We have developed a unique approach towards image restoration that produces images with significant improvements when compared to the original data. We have extended previous results that attempt to determine the intrinsic parameters from the MRI data, and have used these intrinsic parameter images to synthesize MR images. MR images with different TE and TR parameters do not require additional use of an MR scanner, since excellent synthetic MR images are obtained using the restored proton density and nuclear relaxation time images.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

TL;DR: A novel technique for magnetic resonance image (MRI) restoration is introduced, using a physical model (spin equation) and a maximum a posteriori restoration method, based on nonlinear optimization, which reduces noise while preserving resolution.