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 3861–3870 of 3874 papers

TitleStatusHype
Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning—0
Downscaled Representation Matters: Improving Image Rescaling with Collaborative Downscaled Images—0
A deep primal-dual proximal network for image restoration—0
Double U-Net for Super-Resolution and Segmentation of Live Cell Images—0
Double Sparse Multi-Frame Image Super Resolution—0
Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution—0
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI—0
DORNet: A Degradation Oriented and Regularized Network for Blind Depth Super-Resolution—0
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution—0
Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion—0
Show:102550
← PrevPage 387 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified