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 2526–2550 of 3874 papers

TitleStatusHype
Super-resolution GANs of randomly-seeded fieldsCode0
Rotationally Equivariant Super-Resolution of Velocity Fields in Two-Dimensional Fluids Using Convolutional Neural Networks—0
Mathematical Foundation of Sparsity-based Multi-snapshot Spectral Estimation—0
Disentangling Light Fields for Super-Resolution and Disparity Estimation—0
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
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
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision—0
Trained Model in Supervised Deep Learning is a Conditional Risk MinimizerCode0
HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving—0
Patch-Based Stochastic Attention for Image EditingCode0
Accurate super-resolution low-field brain MRI—0
Fast Online Video Super-Resolution with Deformable Attention Pyramid—0
An Optimal Transport Perspective on Unpaired Image Super-Resolution—0
CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial Scalability—0
Scale-arbitrary Invertible Image Downscaling—0
Deep Networks for Image and Video Super-Resolution—0
Image Superresolution using Scale-Recurrent Dense Network—0
End-to-End Optimization of Metasurfaces for Imaging with Compressed SensingCode0
Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super ResolutionCode0
Revisiting L1 Loss in Super-Resolution: A Probabilistic View and Beyond—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified