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 3301–3325 of 3874 papers

TitleStatusHype
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix EstimationCode0
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models—0
CNN-based synthesis of realistic high-resolution LiDAR data—0
Teaching deep neural networks to localize single molecules for super-resolution microscopyCode0
A CNN-Based Super-Resolution Technique for Active Fire Detection on Sentinel-2 Data—0
Resolution-invariant Person Re-IdentificationCode0
DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learningCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy—0
Hierarchical Back Projection Network for Image Super-ResolutionCode0
Exemplar Guided Face Image Super-Resolution without Facial LandmarksCode0
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse ProblemsCode0
On training deep networks for satellite image super-resolution—0
Volumetric Isosurface Rendering with Deep Learning-Based Super-ResolutionCode0
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis—0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
Image-Adaptive GAN based ReconstructionCode0
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression—0
Hybrid Function Sparse Representation towards Image Super ResolutionCode0
Compressed Sensing MRI via a Multi-scale Dilated Residual Convolution Network—0
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior—0
Suppressing Model Overfitting for Image Super-Resolution Networks—0
Convolutional Bipartite Attractor Networks—0
A Multi-Pass GAN for Fluid Flow Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified