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Image Super-Resolution Using Deep Convolutional Networks
We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deepExpand
Learning a Deep Convolutional Network for Image Super-Resolution
TLDR
This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images and shows that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. Expand
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
TLDR
This work thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improves each of them to derive an Enhanced SRGAN (ESRGAN), which achieves consistently better visual quality with more realistic and natural textures than SRGAN. Expand
Accelerating the Super-Resolution Convolutional Neural Network
TLDR
This paper aims at accelerating the current SRCNN, and proposes a compact hourglass-shape CNN structure for faster and better SR, and presents the parameter settings that can achieve real-time performance on a generic CPU while still maintaining good performance. Expand
Compression Artifacts Reduction by a Deep Convolutional Network
TLDR
A compact and efficient network for seamless attenuation of different compression artifacts is formulated and it is demonstrated that a deeper model can be effectively trained with the features learned in a shallow network. Expand
EDVR: Video Restoration With Enhanced Deformable Convolutional Networks
TLDR
This work proposes a novel Video Restoration framework with Enhanced Deformable convolutions, termed EDVR, and proposes a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration. Expand
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
TLDR
This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution. Expand
Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform
TLDR
It is shown that it is possible to recover textures faithful to semantic classes in a single network conditioned on semantic segmentation probability maps through a novel Spatial Feature Transform (SFT) layer that generates affine transformation parameters for spatial-wise feature modulation. Expand
Blind Super-Resolution With Iterative Kernel Correction
TLDR
An iterative correction scheme -- IKC that achieves better results than direct kernel estimation in blind SR problem and an effective SR network architecture using spatial feature transform (SFT) layers to handle multiple blur kernels, named SFTMD. Expand
RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution
TLDR
This work first train a Ranker which can learn the behavior of perceptual metrics and then introduce a novel rank-content loss to optimize the perceptual quality, and shows that RankSRGAN achieves visually pleasing results and reaches state-of-the-art performance in perceptual metrics. Expand
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