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 2126–2150 of 3874 papers

TitleStatusHype
Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI—0
Single Image Super-Resolution Methods: A Survey—0
Mapping molecular complexes with Super-Resolution Microscopy and Single-Particle Analysis—0
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution—0
Optimal Transport for Super Resolution Applied to Astronomy Imaging—0
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problemsCode0
DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRICode0
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in ImagingCode1
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision—0
HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving—0
Trained Model in Supervised Deep Learning is a Conditional Risk MinimizerCode0
Accurate super-resolution low-field brain MRI—0
Patch-Based Stochastic Attention for Image EditingCode0
A new face swap method for image and video domains: a technical reportCode3
TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With TransformersCode1
Fast Online Video Super-Resolution with Deformable Attention Pyramid—0
An Optimal Transport Perspective on Unpaired Image Super-Resolution—0
Gradient Variance Loss for Structure-Enhanced Image Super-ResolutionCode1
CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial Scalability—0
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex RegularizationCode1
Scale-arbitrary Invertible Image Downscaling—0
End-to-End Optimization of Metasurfaces for Imaging with Compressed SensingCode0
Image Superresolution using Scale-Recurrent Dense Network—0
Deep Networks for Image and Video Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified