Image restoration schemes based on the pre-trained deep models have rece...
We present a method for supervised learning of sparsity-promoting
regula...
Recent medical image reconstruction techniques focus on generating
high-...
The recently proposed sparsifying transform models incur low computation...
Reconstruction of CT images from a limited set of projections through an...
We present a method for supervised learning of sparsity-promoting
regula...
There is much recent interest in techniques to accelerate the data
acqui...
Object density reconstruction from projections containing scattered radi...
Many techniques have been proposed for image reconstruction in medical
i...
Achieving high-quality reconstructions from low-dose computed tomography...
Signal models based on sparse representations have received considerable...
Constructing effective image priors is critical to solving ill-posed inv...
We present a method for supervised learning of sparsity-promoting
regula...
Signal models based on sparse representation have received considerable
...
Recent years have witnessed growing interest in machine learning-based m...
Signal models based on sparsity, low-rank and other properties have been...
The field of image reconstruction has undergone four waves of methods. T...
Magnetic resonance imaging (MRI) is widely used in clinical practice for...
Dual energy computed tomography (DECT) imaging plays an important role i...
Learned data models based on sparsity are widely used in signal processi...
Sparsity and low-rank models have been popular for reconstructing images...
Sparsity-based models and techniques have been exploited in many signal
...
Methods exploiting sparsity have been popular in imaging and signal
proc...
Techniques exploiting the sparsity of images in a transform domain have ...
A major challenge in computed tomography (CT) is to reduce X-ray dose to...
The development of computed tomography (CT) image reconstruction methods...
Sparsity-based approaches have been popular in many applications in imag...
Compressed sensing is a powerful tool in applications such as magnetic
r...
Natural signals and images are well-known to be approximately sparse in
...
Many applications in signal processing benefit from the sparsity of sign...