✦ LIBER ✦

Pixon-based image denoising with Markov random fields

✍ Scribed by Qing Lu; Tianzi Jiang

Publisher: Elsevier Science
Year: 2001
Tongue: English
Weight: 576 KB
Volume: 34
Category: Article
ISSN: 0031-3203
DOI: 10.1016/s0031-3203(00)00125-4

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Effective Image Segmentation with Flexib

Effective Image Segmentation with Flexible ICM-Based Markov Random Fields in Distributed Systems of Personal Computers

✍ Odemir Martinez Bruno; Luciano da Fontoura Costa 📂 Article 📅 2000 🏛 Elsevier Science 🌐 English ⚖ 540 KB

T his paper presents the implementation of modi®ed Markov Random Fields (MRFs) in distributed systems of personal computers. Gibbs Random Fields (GRFs) operating in the iterated conditional mode (ICM), modi®ed to incorporate the ¯exibility of selecting from a continuum of con®gurations ranging from

Multi-dimensional multivariate Gaussian

Multi-dimensional multivariate Gaussian Markov random fields with application to image processing

✍ K.V Mardia 📂 Article 📅 1988 🏛 Elsevier Science 🌐 English ⚖ 754 KB

Circuit realization of Markov random fie

Circuit realization of Markov random fields for analog image processing

✍ Parodi, Mauro; Storace, Marco; Regazzoni, Carlo 📂 Article 📅 1998 🏛 John Wiley and Sons 🌐 English ⚖ 428 KB

A methodological approach to the definition of nonlinear circuits for real-time image processing is presented. The image processing problem is formulated in terms of the minimization of a functional based on the Markov random fields (MRFs) theory. The terms of such a functional are related to the co

Image registration using Markov random c

Image registration using Markov random coefficient and geometric transformation fields

✍ Edgar R. Arce-Santana; Alfonso Alba 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 851 KB

Image registration is central to different applications such as medical analysis, biomedical systems, and image guidance. In this paper we propose a new algorithm for multimodal image registration. A Bayesian formulation is presented in which a likelihood term is defined using an observation model b

Brain MRI tissue classification based on

Brain MRI tissue classification based on local Markov random fields

✍ Jussi Tohka; Ivo D. Dinov; David W. Shattuck; Arthur W. Toga 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 899 KB

A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same c

NOTE: Blind Restoration of Degraded Bina

NOTE: Blind Restoration of Degraded Binary Markov Random Field Images

✍ Bing Zhang; Mehdi N. Shirazi; Hideki Noda 📂 Article 📅 1996 🏛 Elsevier Science 🌐 English ⚖ 415 KB

ori (MAP) estimate of the true image and use the iterated conditional modes (ICM) method [1] to generate a MAP The problem of restoring binary images degraded by regiondependent flip-flap noises is considered. The real image is deestimate. The same approach has been adopted in [2], scribed by a Mark