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 2181–2190 of 3874 papers

TitleStatusHype
CT-image Super Resolution Using 3D Convolutional Neural Network—0
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering—0
Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models—0
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution—0
AdaDiffSR: Adaptive Region-aware Dynamic Acceleration Diffusion Model for Real-World Image Super-Resolution—0
CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning—0
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES—0
Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems—0
Low-Complexity Super-Resolution Signature Estimation of XL-MIMO FMCW Radar—0
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified