SOTAVerified

Super-Resolution

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 )

Papers

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Showing 3526–3550 of 3874 papers

TitleStatusHype
Latent Diffusion, Implicit Amplification: Efficient Continuous-Scale Super-Resolution for Remote Sensing ImagesCode0
SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias ClassificationCode0
Polynomial-time Sparse Measure Recovery: From Mean Field Theory to Algorithm DesignCode0
A Review of Convolutional Neural Networks for Inverse Problems in ImagingCode0
Unpaired Depth Super-Resolution in the WildCode0
DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-ResolutionCode0
LAR-SR: A Local Autoregressive Model for Image Super-ResolutionCode0
Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-ResolutionCode0
Practical Manipulation Model for Robust Deepfake DetectionCode0
Laplacian Pyramid-like AutoencoderCode0
Unsupervised and Unregistered Hyperspectral Image Super-Resolution with Mutual Dirichlet-NetCode0
EarthGen: Generating the World from Top-Down ViewsCode0
E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information FlowCode0
Dynamic Structured Illumination Microscopy with a Neural Space-time ModelCode0
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled VariablesCode0
Kernel Modeling Super-Resolution on Real Low-Resolution ImagesCode0
Kernel-aware Burst Blind Super-ResolutionCode0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural OperatorsCode0
Unsupervised Blur Kernel Estimation and Correction for Blind Super-ResolutionCode0
TPU-GAN: Learning temporal coherence from dynamic point cloud sequencesCode0
DWA: Differential Wavelet Amplifier for Image Super-ResolutionCode0
A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images OnlyCode0
Dual-Stream Fusion Network for Spatiotemporal Video Super-ResolutionCode0
Trainable Loss Weights in Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified