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 2976–3000 of 3874 papers

TitleStatusHype
SASNet: Spatially-Adaptive Sinusoidal Neural Networks—0
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models—0
Satellite Image Small Target Application Based on Deep Segmented Residual Neural Network—0
SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution—0
Scalable image coding based on epitomes—0
Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning—0
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks—0
Scale-arbitrary Invertible Image Downscaling—0
A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography—0
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images—0
Use of triplet loss for facial restoration in low-resolution images—0
Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution—0
Scale-Transferrable Object Detection—0
A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded—0
USF Spectral Estimation: Prevalence of Gaussian Cramér-Rao Bounds Despite Modulo Folding—0
SCAN-MUSIC: An Efficient Super-resolution Algorithm for Single Snapshot Wide-band Line Spectral Estimation—0
Scene Prior Filtering for Depth Super-Resolution—0
ARIN: Adaptive Resampling and Instance Normalization for Robust Blind Inpainting of Dunhuang Cave Paintings—0
A Review of Deep Learning Based Image Super-resolution Techniques—0
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks—0
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows—0
Using super-resolution for enhancing visual perception and segmentation performance in veterinary cytology—0
Screentone-Aware Manga Super-Resolution Using DeepLearning—0
SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified