Resistivity Image Reconstruction Using Interacting Dual-Mode Regularization
نویسندگان
چکیده
منابع مشابه
CT Image Reconstruction from Sparse Projections Using Adaptive TpV Regularization
Radiation dose reduction without losing CT image quality has been an increasing concern. Reducing the number of X-ray projections to reconstruct CT images, which is also called sparse-projection reconstruction, can potentially avoid excessive dose delivered to patients in CT examination. To overcome the disadvantages of total variation (TV) minimization method, in this work we introduce a novel...
متن کاملFast image reconstruction with L2-regularization.
PURPOSE We introduce L2-regularized reconstruction algorithms with closed-form solutions that achieve dramatic computational speed-up relative to state of the art L1- and L2-based iterative algorithms while maintaining similar image quality for various applications in MRI reconstruction. MATERIALS AND METHODS We compare fast L2-based methods to state of the art algorithms employing iterative ...
متن کاملRobust Laplacian Regularization for Enhanced Image Reconstruction
This paper presents a new robust regularization approach to the reconstruction of enhanced images from noisy observations. A new regularization constraint designed explicitly to boost nonnoise fine image details is optimized together with a traditional two-term (smooth and fidelity) regularization functional. A gradient descent based numerical solution is developed which is shown to be numerica...
متن کاملUltra-Fast Image Reconstruction of Tomosynthesis Mammography Using GPU
Digital Breast Tomosynthesis (DBT) is a technology that creates three dimensional (3D) images of breast tissue. Tomosynthesis mammography detects lesions that are not detectable with other imaging systems. If image reconstruction time is in the order of seconds, we can use Tomosynthesis systems to perform Tomosynthesis-guided Interventional procedures. This research has been designed to study u...
متن کاملFair-view image reconstruction with dual dictionaries.
In this paper, we formulate the problem of computed tomography (CT)under sparsity and few-view constraints, and propose a novel algorithm for image reconstruction from few-view data utilizing the simultaneous algebraic reconstruction technique (SART) coupled with dictionary learning, sparse representation and total variation (TV) minimization on two interconnected levels. The main feature of ou...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of IKEEE
سال: 2016
ISSN: 1226-7244
DOI: 10.7471/ikeee.2016.20.2.152