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 3251–3260 of 3874 papers

TitleStatusHype
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution—0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
All-in-One Deep Learning Framework for MR Image Reconstruction—0
Video Super Resolution Based on Deep Learning: A Comprehensive Survey—0
STAR-Pose: Efficient Low-Resolution Video Human Pose Estimation via Spatial-Temporal Adaptive Super-Resolution—0
STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
STARS: Sparse Learning Correlation Filter with Spatio-temporal Regularization and Super-resolution Reconstruction for Thermal Infrared Target Tracking—0
State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications—0
Regional climate risk assessment from climate models using probabilistic machine learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified