Titlebook: Scale Space and Variational Methods in Computer Vision; 4th International Co Arjan Kuijper,Kristian Bredies,Horst Bischof Conference procee

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Targeted Iterative Filtering require entirely different behavior of the applied denoising method. In this paper we propose a novel, nonlinear diffusion scheme that is derived from a linear diffusion process in a value space determined by the application. We show that application-driven linear diffusion in the transformed space

Conflagration

Generalized Gradient on Vector Bundle – Application to Image Denoisingrmined by three geometric data: a Riemannian metric on the base manifold, a Riemannian metric and a covariant derivative on the vector bundle. Under the assumption that the covariant derivative is compatible with the metric of the vector bundle, we consider the problems of minimizing the L2 and L1 n

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Variational Methods for Motion Deblurring with Still Backgroundwe propose a model for the formation of this kind of partly blurred images which involve four unknown quantities: The object, the background, the blur kernel and a mask that encodes the shape of the object. Then we propose variational methods to solve the deblurring problem. We show that the method

SIT

肌肉

A Cascadic Alternating Krylov Subspace Image Restoration Methodng is carried out with a wavelet transform, which also provides an estimate of the noise-level. The latter is used to determine a suitable regularization parameter for the Krylov subspace iterative deblurring method. The cascadic multilevel method proceed from coarse to fine image resolution, using

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B-SMART: Bregman-Based First-Order Algorithms for Non-negative Compressed Sensing Problemsve scheme employing non-quadratic proximal terms. This scheme yields closed-form multiplicative updates and handles constraints implicitly. Its analysis does not rely on global Lipschitz continuity in contrast to established state-of-the-art gradient-based methods, hence it is attractive for dealing