765 citations · 1.5k across the 13 of their papers we have counts for
13 papers · 1 filter
Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis
Ziad Al-Halah, Andrew Aitken, Wenzhe Shi +1
Due to the lack of large-scale datasets, the prevailing approach in visual sentiment analysis is to leverage models trained for object classification in large datasets like ImageNe…
Generalizing Deep Learning MRI Reconstruction across Different Domains
Cheng Ouyang, Jo Schlemper, Carlo Biffi +5
We look into the robustness of deep learning based MRI reconstruction when tested on unseen contrasts and organs. We then propose to generalize the network by training with large p…
Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
Chen Qin, Jo Schlemper, Jose Caballero +3
Accelerating the data acquisition of dynamic magnetic resonance imaging (MRI) leads to a challenging ill-posed inverse problem, which has received great interest from both the sign…
Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks
Joost van Amersfoort, Wenzhe Shi, Alejandro Acosta +4
Frame interpolation attempts to synthesise frames given one or more consecutive video frames. In recent years, deep learning approaches, and notably convolutional neural networks,…
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize
Andrew Aitken, Christian Ledig, Lucas Theis +3
The most prominent problem associated with the deconvolution layer is the presence of checkerboard artifacts in output images and dense labels. To combat this problem, smoothness c…
Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
Ozan Oktay, Enzo Ferrante, Konstantinos Kamnitsas +10
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where image…
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Jo Schlemper, Jose Caballero, Joseph V. Hajnal +2
Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2D cardiac magnetic resonance (MR) images from undersampled data using…
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
Jo Schlemper, Jose Caballero, Joseph V. Hajnal +2
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from under…
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
Jose Caballero, Christian Ledig, Andrew Aitken +4
Convolutional neural networks have enabled accurate image super-resolution in real-time. However, recent attempts to benefit from temporal correlations in video super-resolution ha…
Amortised MAP Inference for Image Super-resolution
Casper Kaae Sønderby, Jose Caballero, Lucas Theis +2
Image super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high-resolution images can explain the same downsampled image. Most current sin…
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi, Jose Caballero, Ferenc Huszár +5
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-res…
Is the deconvolution layer the same as a convolutional layer?
Wenzhe Shi, Jose Caballero, Lucas Theis +4
In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented. Firstly, What is the relationship between our propos…
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig, Lucas Theis, Ferenc Huszar +8
Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved…