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 51–75 of 3874 papers

TitleStatusHype
General Geospatial Inference with a Population Dynamics Foundation ModelCode3
ESRGAN: Enhanced Super-Resolution Generative Adversarial NetworksCode3
AudioSR: Versatile Audio Super-resolution at ScaleCode3
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and AlignmentCode3
Event-Enhanced Blurry Video Super-ResolutionCode3
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory DistillationCode3
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge ReportCode3
Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-ResolutionCode2
Arbitrary-Scale Video Super-Resolution with Structural and Textural PriorsCode2
Effective Diffusion Transformer Architecture for Image Super-ResolutionCode2
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-ResourceCode2
Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion ModelsCode2
Dual Aggregation Transformer for Image Super-ResolutionCode2
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-ResolutionCode2
EAMamba: Efficient All-Around Vision State Space Model for Image RestorationCode2
Efficient Face Super-Resolution via Wavelet-based Feature Enhancement NetworkCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
AnimeSR: Learning Real-World Super-Resolution Models for Animation VideosCode2
DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-ResolutionCode2
Enhancing Video Super-Resolution via Implicit Resampling-based AlignmentCode2
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
Distillation-Free One-Step Diffusion for Real-World Image Super-ResolutionCode2
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-ResolutionCode2
Denoising Diffusion Restoration ModelsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified