Image reconstruction: From sparsity to data-adaptive methods and machine learning
The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
Structured overcomplete sparsifying transform learning with convergence guarantees and applications
In recent years, sparse signal modeling, especially using the synthesis model has been
popular. Sparse coding in the synthesis model is however, NP-hard. Recently, interest has …
popular. Sparse coding in the synthesis model is however, NP-hard. Recently, interest has …
PWLS-ULTRA: An efficient clustering and learning-based approach for low-dose 3D CT image reconstruction
The development of computed tomography (CT) image reconstruction methods that
significantly reduce patient radiation exposure, while maintaining high image quality is an …
significantly reduce patient radiation exposure, while maintaining high image quality is an …
Efficient blind compressed sensing using sparsifying transforms with convergence guarantees and application to magnetic resonance imaging
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 …
domains such as wavelets and discrete cosine transform. This property has been heavily …
Sparsifying transform learning with efficient optimal updates and convergence guarantees
Many applications in signal processing benefit from the sparsity of signals in a certain
transform domain or dictionary. Synthesis sparsifying dictionaries that are directly adapted to …
transform domain or dictionary. Synthesis sparsifying dictionaries that are directly adapted to …
Online sparsifying transform learning—Part I: Algorithms
Techniques exploiting the sparsity of signals in a transform domain or dictionary have been
popular in signal processing. Adaptive synthesis dictionaries have been shown to be useful …
popular in signal processing. Adaptive synthesis dictionaries have been shown to be useful …
Data-driven learning of a union of sparsifying transforms model for blind compressed sensing
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 …
(MRI). It enables accurate recovery of images from highly undersampled measurements by …
Sparse-view cone beam CT reconstruction using data-consistent supervised and adversarial learning from scarce training data
Reconstruction of CT images from a limited set of projections through an object is important
in several applications ranging from medical imaging to industrial settings. As the number of …
in several applications ranging from medical imaging to industrial settings. As the number of …
Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography
D Karimi, RK Ward - International journal of computer assisted radiology …, 2016 - Springer
Purpose Image models are central to all image processing tasks. The great advancements
in digital image processing would not have been made possible without powerful models …
in digital image processing would not have been made possible without powerful models …
VIDOSAT: High-dimensional sparsifying transform learning for online video denoising
Techniques exploiting the sparsity of images in a transform domain are effective for various
applications in image and video processing. In particular, transform learning methods …
applications in image and video processing. In particular, transform learning methods …