Total variation (TV) methods have been proposed to improve the image quality in count-
reduced images, by reducing the variation between neighboring pixels. Although very easy …

MRI has recently been identified as a promising application for compressed-sensing-like
regularization because of its potential to speed up the acquisition while maintaining the …

Since shearlets provide nearly optimally sparse representations for a large class of functions
that are useful to model natural images, many image processing methods benefit from their …

To reduce the x-ray dose in computerized tomography (CT), many constrained optimization
approaches have been proposed aiming at minimizing a regularizing function that measures …

Due to the ongoing miniaturization of digital camera sensors and the steady increase of the
“number of megapixels”, individual sensor elements of the camera become more sensitive to …

Recent research in compressed sensing of magnetic resonance imaging (CS-MRI)
emphasizes the importance of modeling structured sparsity, either in the acquisition or in the …

The reconstruction from sparse-or few-view projections is one of important problems in
computed tomography limited by the availability or feasibility of a large number of …

Parallel Imaging MRI (pMRI) and Compressive Sensing (CS) are two reconstruction
techniques that have recently been applied to increase MRI performance. In this paper we …

In computerized tomography, it is important to reduce the image noise without increasing the
acquisition dose. Extensive research has been done into total variation minimization for …

Compressed sensing (CS) has been utilized for acceleration of data acquisition in magnetic
resonance imaging (MRI). MR images can then be reconstructed with an undersampling …