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 3676–3700 of 3874 papers

TitleStatusHype
MemNet: A Persistent Memory Network for Image RestorationCode0
Unsupervised Video Understanding by Reconciliation of Posture Similarities—0
Audio Super Resolution using Neural NetworksCode0
Real-time Deep Video DeinterlacingCode0
Depth Super-Resolution Meets Uncalibrated Photometric StereoCode0
A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames—0
A unified method for super-resolution recovery and real exponential-sum separation—0
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning—0
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception ModuleCode0
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in NetworkCode0
High-Quality Face Image SR Using Conditional Generative Adversarial NetworksCode0
End-to-End Learning of Video Super-Resolution with Motion Compensation—0
Hyperspectral Image Super-Resolution via Non-Local Sparse Tensor Factorization—0
Image Super-Resolution via Deep Recursive Residual NetworkCode0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Adversarial Image Alignment and Interpolation—0
Super-Resolution via Deep Learning—0
Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture—0
Multi-frame image super-resolution with fast upscaling technique—0
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI—0
A New Adaptive Video Super-Resolution Algorithm With Improved Robustness to Innovations—0
Deep Learning for Isotropic Super-Resolution from Non-Isotropic 3D Electron Microscopy—0
Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision—0
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel RemovalCode0
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled VariablesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified