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 241–250 of 3874 papers

TitleStatusHype
OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offsCode1
RAP-SR: RestorAtion Prior Enhancement in Diffusion Models for Realistic Image Super-ResolutionCode1
Neural Garment Dynamic Super-ResolutionCode1
Jointly RS Image Deblurring and Super-Resolution with Adjustable-Kernel and Multi-Domain AttentionCode1
TASR: Timestep-Aware Diffusion Model for Image Super-ResolutionCode1
RFSR: Improving ISR Diffusion Models via Reward Feedback LearningCode1
SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial TranscriptomicsCode1
VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion ModelsCode1
Vision Mamba Distillation for Low-resolution Fine-grained Image ClassificationCode1
MAT: Multi-Range Attention Transformer for Efficient Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified