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 691–700 of 3874 papers

TitleStatusHype
ASSR-NeRF: Arbitrary-Scale Super-Resolution on Voxel Grid for High-Quality Radiance Fields Reconstruction—0
Efficient Event Stream Super-Resolution with Recursive Multi-Branch FusionCode0
Super-resolution imaging using super-oscillatory diffractive neural networks—0
Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-ResolutionCode1
V2X Sidelink Positioning in FR1: From Ray-Tracing and Channel Estimation to Bayesian Tracking—0
A Near-Field Super-Resolution Network for Accelerating Antenna Characterization—0
Burst Image Super-Resolution with Base Frame Selection—0
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution—0
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-ResolutionCode1
Improving Generative Adversarial Networks for Video Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified