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 321–330 of 3874 papers

TitleStatusHype
Generalized and Efficient 2D Gaussian Splatting for Arbitrary-scale Super-ResolutionCode2
Real-Time Neural-Enhancement for Online Cloud Gaming—0
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution—0
StructSR: Refuse Spurious Details in Real-World Image Super-ResolutionCode1
Bit-depth color recovery via off-the-shelf super-resolution models—0
FLowHigh: Towards Efficient and High-Quality Audio Super-Resolution with Single-Step Flow MatchingCode2
Physics-Informed Super-Resolution Diffusion for 6D Phase Space Diagnostics—0
HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based Data Augmentation—0
STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution—0
Conditional Mutual Information Based Diffusion Posterior Sampling for Solving Inverse Problems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified