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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2471–2480 of 3874 papers

TitleStatusHype
RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content—0
S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting—0
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process—0
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis—0
Sampling Based Scene-Space Video Processing—0
SASNet: Spatially-Adaptive Sinusoidal Neural Networks—0
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models—0
Satellite Image Small Target Application Based on Deep Segmented Residual Neural Network—0
SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution—0
Scalable image coding based on epitomes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified