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 261–270 of 3874 papers

TitleStatusHype
SuperGS: Super-Resolution 3D Gaussian Splatting Enhanced by Variational Residual Features and Uncertainty-Augmented LearningCode1
PixelShuffler: A Simple Image Translation Through Pixel RearrangementCode1
Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEsCode1
BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical FlowCode1
PlainUSR: Chasing Faster ConvNet for Efficient Super-ResolutionCode1
Learned Compression for Images and Point CloudsCode1
EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-ResolutionCode1
LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-ResolutionCode1
Rethinking Image Super-Resolution from Training Data PerspectivesCode1
Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven ApproachCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified