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 3476–3500 of 3874 papers

TitleStatusHype
Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data—0
Generative adversarial network-based image super-resolution using perceptual content lossesCode0
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual QualityCode0
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution—0
Deep MR Image Super-Resolution Using Structural Priors—0
Super-Resolution Perception for Industrial Sensor Data—0
Modelling Point Spread Function in Fluorescence Microscopy with a Sparse Combination of Gaussian Mixture: Trade-off between Accuracy and Efficiency—0
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial NetworksCode0
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network—0
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural NetworksCode0
Multi-scale Residual Network for Image Super-ResolutionCode0
Task-Aware Image Downscaling—0
SRFeat: Single Image Super-Resolution with Feature Discrimination—0
Face Super-resolution Guided by Facial Component Heatmaps—0
Spatio-temporal Transformer Network for Video Restoration—0
Super-Resolution and Sparse View CT Reconstruction—0
SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network—0
Learning Sparse Low-Precision Neural Networks With Learnable Regularization—0
ESRGAN: Enhanced Super-Resolution Generative Adversarial NetworksCode3
Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability—0
Wide Activation for Efficient and Accurate Image Super-ResolutionCode0
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis—0
Efficient Single Image Super Resolution using Enhanced Learned Group ConvolutionsCode0
MSCE: An edge preserving robust loss function for improving super-resolution algorithms—0
Improving Super-Resolution Methods via Incremental Residual LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified