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 1851–1860 of 3874 papers

TitleStatusHype
Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN—0
Infrared Image Super-Resolution via Lightweight Information Split Network—0
L^2FMamba: Lightweight Light Field Image Super-Resolution with State Space Model—0
FAN: Feature Adaptation Network for Surveillance Face Recognition and Normalization—0
Context Reasoning Attention Network for Image Super-Resolution—0
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution—0
Fair Primal Dual Splitting Method for Image Inverse Problems—0
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution—0
Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning—0
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution—0
Show:102550
← PrevPage 186 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified