Enhancenet: Single image super-resolution through automated texture synthesis
MSM Sajjadi, B Scholkopf… - Proceedings of the IEEE …, 2017 - openaccess.thecvf.com
Single image super-resolution is the task of inferring a high-resolution image from a single
low-resolution input. Traditionally, the performance of algorithms for this task is measured …
low-resolution input. Traditionally, the performance of algorithms for this task is measured …
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 …
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 …
Edge-based loss function for single image super-resolution
G Seif, D Androutsos - 2018 IEEE International conference on …, 2018 - ieeexplore.ieee.org
In recent years, convolutional neural networks have shown state-of-the-art performance on
the task of single-image super-resolution. Although these proposed networks have shown …
the task of single-image super-resolution. Although these proposed networks have shown …
Structure-preserving image super-resolution via contextualized multitask learning
Single-image super-resolution (SR), which refers to reconstructing a higher resolution image
from the observed lowresolution (LR) image, has received substantial attention due to its …
from the observed lowresolution (LR) image, has received substantial attention due to its …
Difficulty-aware image super resolution via deep adaptive dual-network
Recently, deep learning based single image super-resolution (SR) approaches have
achieved great development. The state-of-the-art SR methods usually adopt a feed-forward …
achieved great development. The state-of-the-art SR methods usually adopt a feed-forward …
Edge-aware image super-resolution using a generative adversarial network
Edge-awareness is an important factor in the perception of high frequency details. MSE-
based single image super-resolution (SISR) algorithms, such as SRResNet do not deliver …
based single image super-resolution (SISR) algorithms, such as SRResNet do not deliver …
Internal learning for image super-resolution by adaptive feature transform
Y He, W Cao, X Du, C Chen - Symmetry, 2020 - mdpi.com
Recent years have witnessed the great success of image super-resolution based on deep
learning. However, it is hard to adapt a well-trained deep model for a specific image for …
learning. However, it is hard to adapt a well-trained deep model for a specific image for …
Learning image blind denoisers without explicit noise modeling
Image blind denoising aims at removing the unknown noise from given images to improve
the image's visual quality. Current blind denoisers can be categorized into two classes, ie …
the image's visual quality. Current blind denoisers can be categorized into two classes, ie …
A quantitative comparison on file folder structures of two groups of information workers
H Zhang, X Hu - IEEE/ACM Joint Conference on Digital …, 2014 - ieeexplore.ieee.org
This study compares file folder structures on personal computers of two groups of
information workers, administrative staff and PhD students. A set of quantitative measures …
information workers, administrative staff and PhD students. A set of quantitative measures …