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 )
Benchmarks
These leaderboards are used to track progress in Super-Resolution
Datasets
Subtasks
Most implemented papers
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
To solve these problems, we propose the very deep residual channel attention networks (RCAN).
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
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
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
The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces.
Residual Dense Network for Image Super-Resolution
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
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
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
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
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
We introduce several techniques for sampling and visualizing the latent spaces of generative models.