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 3341–3350 of 3874 papers

TitleStatusHype
Lightweight Image Super-Resolution with Adaptive Weighted Learning NetworkCode0
Guided Super-Resolution as Pixel-to-Pixel TransformationCode1
CFSNet: Toward a Controllable Feature Space for Image RestorationCode0
Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model—0
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study—0
A HVS-inspired Attention to Improve Loss Metrics for CNN-based Perception-Oriented Super-Resolution—0
Deep Plug-and-Play Super-Resolution for Arbitrary Blur KernelsCode1
Computational structured illumination for high-content fluorescent and phase microscopy—0
Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-Resolution Network—0
Recurrent Back-Projection Network for Video Super-ResolutionCode0
Show:102550
← PrevPage 335 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified