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 81–90 of 3874 papers

TitleStatusHype
Blind Restoration of High-Resolution Ultrasound Video—0
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to RankCode2
Enhancing Diffusion-Weighted Images (DWI) for Diffusion MRI: Is it Enough without Non-Diffusion-Weighted B=0 Reference?—0
Trustworthy Image Super-Resolution via Generative PseudoinverseCode0
CLIP-aware Domain-Adaptive Super-Resolution—0
Redefining Neural Operators in d+1 Dimensions—0
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling—0
UGoDIT: Unsupervised Group Deep Image Prior Via Transferable WeightsCode0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified