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 621–630 of 3874 papers

TitleStatusHype
Palantir: Towards Efficient Super Resolution for Ultra-high-definition Live StreamingCode0
SSL: A Self-similarity Loss for Improving Generative Image Super-resolutionCode2
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
Underwater litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network—0
Monitoring of Hermit Crabs Using drone-captured imagery and Deep Learning based Super-Resolution Reconstruction and Improved YOLOv8—0
SGSR: Structure-Guided Multi-Contrast MRI Super-Resolution via Spatio-Frequency Co-Query Attention—0
Supervised Image Translation from Visible to Infrared Domain for Object Detection—0
PINNs for Medical Image Analysis: A Survey—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified