Robust single image super-resolution via deep networks with sparse prior
Single image super-resolution (SR) is an ill-posed problem, which tries to recover a high-
resolution image from its low-resolution observation. To regularize the solution of the …
resolution image from its low-resolution observation. To regularize the solution of the …
Image restoration via simultaneous nonlocal self-similarity priors
Through exploiting the image nonlocal self-similarity (NSS) prior by clustering similar
patches to construct patch groups, recent studies have revealed that structural sparse …
patches to construct patch groups, recent studies have revealed that structural sparse …
When image denoising meets high-level vision tasks: A deep learning approach
Conventionally, image denoising and high-level vision tasks are handled separately in
computer vision. In this paper, we cope with the two jointly and explore the mutual influence …
computer vision. In this paper, we cope with the two jointly and explore the mutual influence …
Ground-based image analysis: A tutorial on machine-learning techniques and applications
Ground-based whole-sky cameras have opened up new opportunities for monitoring the
earth's atmosphere. These cameras are an important complement to satellite images by …
earth's atmosphere. These cameras are an important complement to satellite images by …
Fast multiclass dictionaries learning with geometrical directions in MRI reconstruction
Objective: Improve the reconstructed image with fast and multiclass dictionaries learning
when magnetic resonance imaging is accelerated by undersampling the k-space data …
when magnetic resonance imaging is accelerated by undersampling the k-space data …
Simultaneous detection of multiple appliances from smart-meter measurements via multi-label consistent deep dictionary learning and deep transform learning
Currently there are several well-known approaches to non-intrusive appliance load
monitoring-rule based, stochastic finite state machines, neural networks, and sparse coding …
monitoring-rule based, stochastic finite state machines, neural networks, and sparse coding …
Blind denoising autoencoder
A Majumdar - IEEE transactions on neural networks and …, 2018 - ieeexplore.ieee.org
The term “blind denoising” refers to the fact that the basis used for denoising is learned from
the noisy sample itself during denoising. Dictionary learning-and transform learning-based …
the noisy sample itself during denoising. Dictionary learning-and transform learning-based …
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 …
Trainlets: Dictionary learning in high dimensions
Sparse representation has shown to be a very powerful model for real world signals, and
has enabled the development of applications with notable performance. Combined with the …
has enabled the development of applications with notable performance. Combined with the …
QDataSet, quantum datasets for machine learning
The availability of large-scale datasets on which to train, benchmark and test algorithms has
been central to the rapid development of machine learning as a discipline. Despite …
been central to the rapid development of machine learning as a discipline. Despite …