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
Latest papers with no code
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network
Image super-resolution has been an important subject in image processing and recognition.
Climate Downscaling: A Deep-Learning Based Super-resolution Model of Precipitation Data with Attention Block and Skip Connections
In Taiwan, although the average annual precipitation is up to 2, 500 millimeter (mm), the water allocation for each person is lower than the global average due to drastically geographical elevation changes and uneven distribution through the year.
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder
We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks.
Self-Adaptive Reality-Guided Diffusion for Artifact-Free Super-Resolution
Following this, the Reality Guidance Refinement (RGR) process refines artifacts by integrating this mask with realistic latent representations, improving alignment with the original image.
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution
Technically, SATeCo freezes all the parameters of the pre-trained UNet and VAE, and only optimizes two deliberately-designed spatial feature adaptation (SFA) and temporal feature alignment (TFA) modules, in the decoder of UNet and VAE.
Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy
The use of fluorescent molecules to create long sequences of low-density, diffraction-limited images enables highly-precise molecule localization.
A Study in Dataset Pruning for Image Super-Resolution
We introduce a novel approach that reduces a dataset to a core-set of training samples, selected based on their loss values as determined by a simple pre-trained SR model.
Time-series Initialization and Conditioning for Video-agnostic Stabilization of Video Super-Resolution using Recurrent Networks
The proposed training strategy stabilizes VSR by training a VSR network with various RNN hidden states changed depending on the video properties.
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping
In this work, we developed a 3D image patch-based diffusion model, namely QSMDiff, for robust QSM reconstruction across different scan parameters, alongside simultaneous super-resolution and image-denoising tasks.
Hyperspectral Neural Radiance Fields
Hyperspectral Imagery (HSI) has been used in many applications to non-destructively determine the material and/or chemical compositions of samples.