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 701–725 of 3874 papers

TitleStatusHype
Gridless Parameter Estimation in Partly Calibrated Rectangular Arrays—0
Mamba-based Light Field Super-Resolution with Efficient Subspace Scanning—0
Learning Accurate and Enriched Features for Stereo Image Super-ResolutionCode0
Zero-Shot Image Denoising for High-Resolution Electron MicroscopyCode1
EvTexture: Event-driven Texture Enhancement for Video Super-ResolutionCode5
Enhance the Image: Super Resolution using Artificial Intelligence in MRI—0
IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting Transformers in Real-World Image Super-ResolutionCode0
Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal Image Super Resolution—0
LFMamba: Light Field Image Super-Resolution with State Space Model—0
A Dictionary Based Approach for Removing Out-of-Focus BlurCode0
Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution—0
Bayesian Conditioned Diffusion Models for Inverse Problems—0
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models—0
GaussianSR: 3D Gaussian Super-Resolution with 2D Diffusion Priors—0
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-ResolutionCode1
DDR: Exploiting Deep Degradation Response as Flexible Image DescriptorCode0
One-Step Effective Diffusion Network for Real-World Image Super-ResolutionCode4
Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance—0
Image Neural Field Diffusion Models—0
Towards Realistic Data Generation for Real-World Super-Resolution—0
2DQuant: Low-bit Post-Training Quantization for Image Super-ResolutionCode1
Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning—0
Binarized Diffusion Model for Image Super-ResolutionCode2
M2NO: Multiresolution Operator Learning with Multiwavelet-based Algebraic Multigrid Method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified