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Found 15 papers, 8 papers with code
Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction
We show that the proposed approaches are competitive relative to the state of the art both quantitatively and qualitatively.
Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis
Additionally, we introduce a novel emoji representation based on their visual emotional response which supports a deeper understanding of the emoji modality and their usage on social media.
Deep Hashing using Entropy Regularised Product Quantisation Network
In large scale systems, approximate nearest neighbour search is a crucial algorithm to enable efficient data retrievals.
Generalising Deep Learning MRI Reconstruction across Different Domains
We look into robustness of deep learning based MRI reconstruction when tested on unseen contrasts and organs.
Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations.
Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks
To improve the quality of synthesised intermediate video frames, our network is jointly supervised at different levels with a perceptual loss function that consists of an adversarial and two content losses.
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize
Compared to sub-pixel convolution initialized with schemes designed for standard convolution kernels, it is free from checkerboard artifacts immediately after initialization.
Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge.
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed.
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
Convolutional neural networks have enabled accurate image super-resolution in real-time.
Ranked #11 on
Video Super-Resolution
on MSU Video Upscalers: Quality Enhancement
(VMAF metric)
Amortised MAP Inference for Image Super-resolution
We show that, using this architecture, the amortised MAP inference problem reduces to minimising the cross-entropy between two distributions, similar to training generative models.
Is the deconvolution layer the same as a convolutional layer?
In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented.
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
This means that the super-resolution (SR) operation is performed in HR space.
Ranked #1 on
Video Super-Resolution
on Xiph HD - 4x upscaling
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
Ranked #3 on
Image Super-Resolution
on VggFace2 - 8x upscaling
