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 1551–1560 of 3874 papers

TitleStatusHype
QMambaBSR: Burst Image Super-Resolution with Query State Space Model—0
Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-ResolutionCode0
Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-ResolutionCode0
DIffSteISR: Harnessing Diffusion Prior for Superior Real-world Stereo Image Super-Resolution—0
Event-Stream Super Resolution using Sigma-Delta Neural Network—0
Palantir: Towards Efficient Super Resolution for Ultra-high-definition Live StreamingCode0
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution—0
Kalman-Inspired Feature Propagation for Video Face Super-Resolution—0
Efficient Single Image Super-Resolution with Entropy Attention and Receptive Field Augmentation—0
Monitoring of Hermit Crabs Using drone-captured imagery and Deep Learning based Super-Resolution Reconstruction and Improved YOLOv8—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified