Learning-based optimization of the under-sampling pattern in MRI
Abstract Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated by
under-sampling in k-space (ie, the Fourier domain). In this paper, we consider the problem …
under-sampling in k-space (ie, the Fourier domain). In this paper, we consider the problem …
Sparse signal representation, sampling, and recovery in compressive sensing frameworks
Compressive sensing allows the reconstruction of original signals from a much smaller
number of samples as compared to the Nyquist sampling rate. The effectiveness of …
number of samples as compared to the Nyquist sampling rate. The effectiveness of …
Learning based speech compressive subsampling
In this paper, we present a learning-based approach to speech compressive subsampling.
Prior work in the field has mainly used random or deterministic matrices, which are …
Prior work in the field has mainly used random or deterministic matrices, which are …
Genetic algorithm based framework for optimized sensing matrix design in compressed sensing
Sampling matrices used in compressed sensing framework are mostly randomly structured
and thus inefficient in terms of memory utilization, reconstruction speed, and computational …
and thus inefficient in terms of memory utilization, reconstruction speed, and computational …
Techniques for Efficient Signal Recovery Using Compressive Sensing
I Ahmed - 2022 - search.proquest.com
Compressive Sensing (CS) has been applied in many fields due to its benefits of sparse
signal reconstruction from a far lesser number of signal samples than the traditionally used …
signal reconstruction from a far lesser number of signal samples than the traditionally used …