Charles A. Bouman

TL;DR: Enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies and clinical results illustrate the capabilities of the algorithm on real patient data.

TL;DR: In this article, a generalized Gaussian Markov random field (GGMRF) is proposed for image reconstruction in low-dosage transmission tomography, which satisfies several desirable analytical and computational properties for map estimation, including continuous dependence of the estimate on the data and invariance of the character of solutions to scaling of data.

TL;DR: This paper demonstrates with some simple examples how Plug-and-Play priors can be used to mix and match a wide variety of existing denoising models with a tomographic forward model, thus greatly expanding the range of possible problem solutions.

TL;DR: Simulations on synthetic images indicate that the new algorithm performs better and requires much less computation than MAP estimation using simulated annealing, and is found to improve classification accuracy when applied to the segmentation of multispectral remotely sensed images with ground truth data.

TL;DR: The authors develop algorithms for the design of hierarchical tree structured color palettes incorporating performance criteria which reflect subjective evaluations of image quality, which produce higher-quality displayed images and require fewer computations than previously proposed methods.