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 1541–1550 of 3874 papers

TitleStatusHype
Learning to Super-Resolve Blurry Images with EventsCode1
LSR: A Light-Weight Super-Resolution Method—0
Spatially-Adaptive Feature Modulation for Efficient Image Super-ResolutionCode2
Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information—0
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded ImagesCode1
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI—0
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuningCode1
On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution—0
DISCO: Distributed Inference with Sparse Communications—0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
Show:102550
← PrevPage 155 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified