Super-Resolution

1244 papers with code • 0 benchmarks • 20 datasets

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Most implemented papers

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

yulunzhang/RCAN ECCV 2018

To solve these problems, we propose the very deep residual channel attention networks (RCAN).

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections

titu1994/Image-Super-Resolution 29 Jun 2016

In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers.

Deep Back-Projection Networks For Super-Resolution

sanghyun-son/EDSR-PyTorch CVPR 2018

The feed-forward architectures of recently proposed deep super-resolution networks learn representations of low-resolution inputs, and the non-linear mapping from those to high-resolution output.

Fourier Neural Operator for Parametric Partial Differential Equations

zongyi-li/fourier_neural_operator ICLR 2021

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces.

Residual Dense Network for Image Super-Resolution

yulunzhang/RDN CVPR 2018

In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers.

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

adamian98/pulse CVPR 2020

We present an algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature.

Accelerating the Super-Resolution Convolutional Neural Network

Araxeus/PNG-Upscale 1 Aug 2016

As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality.

Deep Image Prior

DmitryUlyanov/deep-image-prior CVPR 2018

In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

thunil/TecoGAN 23 Nov 2018

Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution.

Sampling Generative Networks

dribnet/plat 14 Sep 2016

We introduce several techniques for sampling and visualizing the latent spaces of generative models.