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 1561–1570 of 3874 papers

TitleStatusHype
Super-Resolution of BVOC Maps by Adapting Deep Learning Methods—0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution—0
Variational Mixture of HyperGenerators for Learning Distributions Over FunctionsCode0
Hyperspectral Image Super Resolution with Real Unaligned RGB GuidanceCode1
I^2SB: Image-to-Image Schrödinger BridgeCode2
Hypernetworks build Implicit Neural Representations of SoundsCode1
Towards Geospatial Foundation Models via Continual PretrainingCode1
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning ConventionsCode0
OSRT: Omnidirectional Image Super-Resolution with Distortion-aware TransformerCode1
High-Resolution GAN Inversion for Degraded Images in Large Diverse DatasetsCode0
Show:102550
← PrevPage 157 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified