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 1681–1690 of 3874 papers

TitleStatusHype
ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold—0
From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-ResolutionCode1
FREDSR: Fourier Residual Efficient Diffusive GAN for Single Image Super Resolution—0
Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantificationCode1
Knowledge Distillation based Degradation Estimation for Blind Super-ResolutionCode1
Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution—0
Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture—0
A mathematical theory of super-resolution and two-point resolution—0
Separation-Free Spectral Super-Resolution via Convex Optimization—0
Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition—0
Show:102550
← PrevPage 169 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified