Roadmap on computational methods in optical imaging and holography
Computational methods have been established as cornerstones in optical imaging and
holography in recent years. Every year, the dependence of optical imaging and holography …
holography in recent years. Every year, the dependence of optical imaging and holography …
Denoising of microscopy images: a review of the state-of-the-art, and a new sparsity-based method
This paper reviews the state-of-the-art in denoising methods for biological microscopy
images and introduces a new and original sparsity-based algorithm. The proposed method …
images and introduces a new and original sparsity-based algorithm. The proposed method …
Deep-learning-based optimization of the under-sampling pattern in MRI
In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to
achieve accelerated scan times. CS-MRI presents two fundamental problems:(1) where to …
achieve accelerated scan times. CS-MRI presents two fundamental problems:(1) where to …
Breaking the coherence barrier: A new theory for compressed sensing
This paper presents a framework for compressed sensing that bridges a gap between
existing theory and the current use of compressed sensing in many real-world applications …
existing theory and the current use of compressed sensing in many real-world applications …
B-spline parameterized joint optimization of reconstruction and k-space trajectories (bjork) for accelerated 2d mri
Optimizing k-space sampling trajectories is a promising yet challenging topic for fast
magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method …
magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method …
Compressed-sensing MRI with random encoding
Compressed sensing (CS) has the potential to reduce magnetic resonance (MR) data
acquisition time. In order for CS-based imaging schemes to be effective, the signal of interest …
acquisition time. In order for CS-based imaging schemes to be effective, the signal of interest …
A gradient-based alternating minimization approach for optimization of the measurement matrix in compressive sensing
In this paper the problem of optimization of the measurement matrix in compressive (also
called compressed) sensing framework is addressed. In compressed sensing a …
called compressed) sensing framework is addressed. In compressed sensing a …
Stable and robust sampling strategies for compressive imaging
In many signal processing applications, one wishes to acquire images that are sparse in
transform domains such as spatial finite differences or wavelets using frequency domain …
transform domains such as spatial finite differences or wavelets using frequency domain …
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 …
Fast data-driven learning of parallel MRI sampling patterns for large scale problems
In this study, a fast data-driven optimization approach, named bias-accelerated subset
selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the …
selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the …