Deep learning for image super-resolution: A survey
Image Super-Resolution (SR) is an important class of image processing techniqueso
enhance the resolution of images and videos in computer vision. Recent years have …
enhance the resolution of images and videos in computer vision. Recent years have …
Sparsity invariant cnns
In this paper, we consider convolutional neural networks operating on sparse inputs with an
application to depth completion from sparse laser scan data. First, we show that traditional …
application to depth completion from sparse laser scan data. First, we show that traditional …
Depth estimation via affinity learned with convolutional spatial propagation network
Depth estimation from a single image is a fundamental problem in computer vision. In this
paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) …
paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) …
Learning depth with convolutional spatial propagation network
In this paper, we propose the convolutional spatial propagation network (CSPN) and
demonstrate its effectiveness for various depth estimation tasks. CSPN is a simple and …
demonstrate its effectiveness for various depth estimation tasks. CSPN is a simple and …
Multimedia super-resolution via deep learning: A survey
K Hayat - Digital Signal Processing, 2018 - Elsevier
The recent phenomenal interest in convolutional neural networks (CNNs) must have made it
inevitable for the super-resolution (SR) community to explore its potential. The response has …
inevitable for the super-resolution (SR) community to explore its potential. The response has …
In defense of classical image processing: Fast depth completion on the cpu
With the rise of data driven deep neural networks as a realization of universal function
approximators, most research on computer vision problems has moved away from …
approximators, most research on computer vision problems has moved away from …
Deep convolutional neural network for multi-modal image restoration and fusion
X Deng, PL Dragotti - IEEE transactions on pattern analysis …, 2020 - ieeexplore.ieee.org
In this paper, we propose a novel deep convolutional neural network to solve the general
multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different …
multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different …
Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data
DR Rutkowski, A Roldán-Alzate, KM Johnson - Scientific reports, 2021 - nature.com
Blood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic
resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular …
resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular …
Channel attention based iterative residual learning for depth map super-resolution
Despite the remarkable progresses made in deep learning based depth map super-
resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps …
resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps …
Super-resolution via deep learning
K Hayat - arXiv preprint arXiv:1706.09077, 2017 - arxiv.org
The recent phenomenal interest in convolutional neural networks (CNNs) must have made it
inevitable for the super-resolution (SR) community to explore its potential. The response has …
inevitable for the super-resolution (SR) community to explore its potential. The response has …