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 26–50 of 3874 papers

TitleStatusHype
The Tenth NTIRE 2025 Efficient Super-Resolution Challenge ReportCode3
CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-ResolutionCode3
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory DistillationCode3
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-ResolutionCode3
General Geospatial Inference with a Population Dynamics Foundation ModelCode3
Degradation-Guided One-Step Image Super-Resolution with Diffusion PriorsCode3
Panacea+: Panoramic and Controllable Video Generation for Autonomous DrivingCode3
Blind Image Restoration via Fast Diffusion InversionCode3
Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion TransformerCode3
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge SurveyCode3
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge ReportCode3
DRCT: Saving Image Super-resolution away from Information BottleneckCode3
VmambaIR: Visual State Space Model for Image RestorationCode3
CAMixerSR: Only Details Need More "Attention"Code3
Lumiere: A Space-Time Diffusion Model for Video GenerationCode3
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-ResolutionCode3
HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech SynthesisCode3
AudioSR: Versatile Audio Super-resolution at ScaleCode3
HAT: Hybrid Attention Transformer for Image RestorationCode3
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized StylizationCode3
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual ShiftingCode3
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
Activating More Pixels in Image Super-Resolution TransformerCode3
A new face swap method for image and video domains: a technical reportCode3
VRT: A Video Restoration TransformerCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified