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 1701–1725 of 3874 papers

TitleStatusHype
Space-Time Video Super-resolution with Neural Operator—0
Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations—0
Gull: A Generative Multifunctional Audio Codec—0
CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data—0
Efficient Learnable Collaborative Attention for Single Image Super-Resolution—0
Power-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprint—0
PointSAGE: Mesh-independent superresolution approach to fluid flow predictions—0
Real-GDSR: Real-World Guided DSM Super-Resolution via Edge-Enhancing Residual Network—0
CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning—0
Translation-based Video-to-Video Synthesis—0
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
Distortion-aware super-resolution for planetary exploration imagesCode0
Super-Resolution Analysis for Landfill Waste Classification—0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
Video Interpolation with Diffusion Models—0
SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral Stereo Image Guided Denoising—0
Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D—0
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network—0
Masked Autoencoders are PDE LearnersCode0
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational AutoencoderCode0
Climate Downscaling: A Deep-Learning Based Super-resolution Model of Precipitation Data with Attention Block and Skip Connections—0
Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution MicroscopyCode0
Self-Adaptive Reality-Guided Diffusion for Artifact-Free Super-ResolutionCode0
A Study in Dataset Pruning for Image Super-Resolution—0
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified