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 1651–1675 of 3874 papers

TitleStatusHype
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
Reference-based Image and Video Super-Resolution via C2-MatchingCode2
NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution—0
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution—0
Meta-Learned Kernel For Blind Super-Resolution Kernel EstimationCode1
Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors—0
Mitigating Artifacts in Real-World Video Super-Resolution ModelsCode1
U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image FusionCode1
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-ResolutionCode1
Neural Volume Super-Resolution—0
SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering—0
Spatio-Temporal Super-Resolution of Dynamical Systems using Physics-Informed Deep-Learning—0
On the Robustness of Normalizing Flows for Inverse Problems in Imaging—0
A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information—0
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
Learning Continuous Depth Representation via Geometric Spatial AggregatorCode1
RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal ShiftsCode1
Super-resolution Probabilistic Rain Prediction from Satellite Data Using 3D U-Nets and EarthFormersCode1
ADIR: Adaptive Diffusion for Image Reconstruction—0
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast CompetitionCode0
Double U-Net for Super-Resolution and Segmentation of Live Cell Images—0
Learning Detail-Structure Alternative Optimization for Blind Super-ResolutionCode1
Bridging Component Learning with Degradation Modelling for Blind Image Super-ResolutionCode1
DiTBN: Detail Injection-Based Two-Branch Network for Pansharpening of Remote Sensing ImagesCode0
Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified