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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1376–1400 of 3874 papers

TitleStatusHype
Deceptive-NeRF/3DGS: Diffusion-Generated Pseudo-Observations for High-Quality Sparse-View Reconstruction—0
NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-World Video Super-Resolution—0
EgoVSR: Towards High-Quality Egocentric Video Super-ResolutionCode1
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection—0
A Dive into SAM Prior in Image Restoration—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems—0
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIsCode1
LeftRefill: Filling Right Canvas based on Left Reference through Generalized Text-to-Image Diffusion ModelCode1
Efficient Mixed Transformer for Single Image Super-ResolutionCode2
Reciprocal Attention Mixing Transformer for Lightweight Image RestorationCode1
mdctGAN: Taming transformer-based GAN for speech super-resolution with Modified DCT spectraCode1
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration ProblemsCode1
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
Screentone-Aware Manga Super-Resolution Using DeepLearning—0
Toward Moiré-Free and Detail-Preserving Demosaicking—0
PanFlowNet: A Flow-Based Deep Network for Pan-sharpening—0
Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution—0
Sensing User's Channel and Location with Terahertz Extra-Large Reconfigurable Intelligent Surface under Hybrid-Field Beam Squint Effect—0
Exploiting Diffusion Prior for Real-World Image Super-ResolutionCode4
Can SAM Boost Video Super-Resolution?—0
Propagation Modeling for Physically Large Arrays: Measurements and Multipath Component Visibility—0
Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified