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 1661–1670 of 3874 papers

TitleStatusHype
Neural Volume Super-Resolution—0
On the Robustness of Normalizing Flows for Inverse Problems in Imaging—0
A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information—0
Spatio-Temporal Super-Resolution of Dynamical Systems using Physics-Informed Deep-Learning—0
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
Learning Continuous Depth Representation via Geometric Spatial AggregatorCode1
RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal ShiftsCode1
Super-resolution Probabilistic Rain Prediction from Satellite Data Using 3D U-Nets and EarthFormersCode1
ADIR: Adaptive Diffusion for Image Reconstruction—0
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast CompetitionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified