Abstract. In this paper we generalize the iterated refinement method, introduced by the authors in [8], to a time-continuous inverse scale-space formulation. The iterated refinement procedure yields a sequence of convex variational problems, evolving toward the noisy image. The inverse scale space

of applications, often being significantly faster (in terms of computation time) than competing methods. Although the performance of GP methods tends to degrade as the regularization term is de-emphasized, we show how they can be embedded in a continuation scheme to recover their efficient practical performance.

is recursively fiood4illed ,o determine the topology of the gray.white matter border, and (3) the resulting continuous surface is refinc by relaxing it against the original 3-D gray-scale image using a deformable template method, which is also used to computationally flatten the cortex for k'asier vic

complementary to Fourier preparation by linear field gradients. Thus, by using multiple receiver coils in parallel scan time in Fourier imaging can be considerably reduced. The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil

the mode occurs at �™��- � aSGHz. Fig. 2(a) ( � aIHGHz) and Fig. 2(b) ( � aPGHz), show the radiated field for frequencies above and below the cutoff, respectively. In both cases, the line integration (continuous line) and the surface integrations (dots) lead to identical results in the limit

Abstract. Continuous time random walks impose a random waiting time before each particle jump. Scaling limits of heavy tailed continuous time random walks are governed by fractional evolution equations. Space-fractional derivatives describe heavy tailed jumps, and the time-fractional version codes

location and interaction models. This was introduced and popularized by Wilson, and it led to many different extensions that elaborate the framework rather than extend it to different kinds of models. I review two such extensions here: how space can be introduced into the formulation through defining a

simulation. Inverse problems are significantly more difficult to solve than forward problems, due to ill-posedness, large dense ill-conditioned operators, multiple minima, space-time coupling, and the need to solve the forward problem repeatedly. We present a parallel algorithm for inverse problems governed

The capacity to predict motion adequately over the time scale of a few seconds is fundamental to autonomous mobility. Model predictive optimal control is a general formalism within which most historical approaches can be cast as special cases. Applications continue to grow in ambition to seek

In this paper, we develop interval estimation methods for means of bounded random variables based on a sequential procedure such that the sampling is continued until the sample sum is no less than a prescribed threshold. 1 Inverse Sampling It is a ubiquitous problem to estimate the means of bounded