Compressed sensing (CS) utilizes the sparsity of magnetic resonance (MR) images to
enable accurate reconstruction from undersampled k-space data. Recent CS methods have …

Image fusion is capable of processing multiple heterogeneous images acquired by single or
multi-sensor imaging systems for an improved interpretation of the targeted object or scene …

Traditional image signal processing (ISP) pipeline consists of a set of cascaded image
processing modules onboard a camera to reconstruct a high-quality sRGB image from the …

We develop a Bayesian nonparametric model for reconstructing magnetic resonance
images (MRIs) from highly undersampled k-space data. We perform dictionary learning as …

Natural signals and images are well known to be approximately sparse in transform
domains such as wavelets and discrete cosine transform. This property has been heavily …

Most of the traditional medical image fusion methods that use the multi-scale decomposition
schemes suffer from the bad image representations and the loss of the dependency in …

Traditional compressed sensing considers sampling a 1D signal. For a multidimensional
signal, if reshaped into a vector, the required size of the sensing matrix becomes …

Sparsity-based approaches have been popular in many applications in image processing
and imaging. Compressed sensing exploits the sparsity of images in a transform domain or …

Compressed sensing is a powerful tool in applications such as magnetic resonance imaging
(MRI). It enables accurate recovery of images from highly undersampled measurements by …

Compressed sensing magnetic resonance imaging (CS-MRI) has exhibited great potential
to accelerate magnetic resonance imaging if an image can be sparsely represented. How to …