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 2091–2100 of 3874 papers

TitleStatusHype
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors—0
Efficient Long-Range Attention Network for Image Super-resolutionCode2
Unfolded Deep Kernel Estimation for Blind Image Super-resolutionCode1
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches—0
Learning the Degradation Distribution for Blind Image Super-ResolutionCode1
Rethinking data-driven point spread function modeling with a differentiable optical modelCode1
Regularized Training of Intermediate Layers for Generative Models for Inverse ProblemsCode0
Fast and selective super-resolution ultrasound in vivo with sono-switchable nanodroplets—0
Sub-Terahertz Channel Measurements and Characterization in a Factory Building—0
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution NetworksCode1
Show:102550
← PrevPage 210 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified