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 1631–1640 of 3874 papers

TitleStatusHype
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution—0
Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer—0
A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport—0
Higher-order MRFs based image super resolution: why not MAP?—0
Feedback Pyramid Attention Networks for Single Image Super-Resolution—0
High-Frequency aware Perceptual Image Enhancement—0
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features—0
Coupled-Projection Residual Network for MRI Super-Resolution—0
Show:102550
← PrevPage 164 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified