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 3571–3580 of 3874 papers

TitleStatusHype
Face Hallucination using Linear Models of Coupled Sparse Support—0
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior—0
Face Recognition in Low Quality Images: A Survey—0
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool—0
Face Super-resolution Guided by Facial Component Heatmaps—0
Embedding Similarity Guided License Plate Super Resolution—0
Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution—0
Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors—0
Face to Cartoon Incremental Super-Resolution using Knowledge Distillation—0
Facial Attribute Capsules for Noise Face Super Resolution—0
Show:102550
← PrevPage 358 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified