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 3576–3600 of 3874 papers

TitleStatusHype
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification—0
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse CodingCode0
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object RepresentationCode0
Self Super-Resolution for Magnetic Resonance Images using Deep Networks—0
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network—0
Residual Dense Network for Image Super-ResolutionCode0
MagnifyMe: Aiding Cross Resolution Face Recognition via Identity Aware Synthesis—0
Composite Optimization by Nonconvex Majorization-Minimization—0
Deep Residual Network for Joint Demosaicing and Super-ResolutionCode0
cGANs with Projection DiscriminatorCode0
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution ForestsCode0
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface GenerationCode0
Image Transformer—0
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep LearningCode0
Deep Image Super Resolution via Natural Image Priors—0
Super-resolution of spatiotemporal event-stream image captured by the asynchronous temporal contrast vision sensor—0
Multispectral Compressive Imaging Strategies using Fabry-Pérot Filtered Sensors—0
Orthogonally Regularized Deep Networks For Image Super-resolution—0
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation NetworksCode0
tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid FlowCode0
Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST—0
Denoising Prior Driven Deep Neural Network for Image RestorationCode0
Frame-Recurrent Video Super-Resolution—0
How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?—0
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified