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

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Showing 3451–3500 of 3874 papers

TitleStatusHype
Channel Splitting Network for Single MR Image Super-Resolution—0
Deep Learning-Based Channel EstimationCode0
Efficient Two-Dimensional Line Spectrum Estimation Based on Decoupled Atomic Norm Minimization—0
Deep Bi-Dense Networks for Image Super-ResolutionCode0
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN—0
Triple Attention Mixed Link Network for Single Image Super Resolution—0
MRI Super-Resolution using Multi-Channel Total VariationCode0
Recurrent Transition Networks for Character LocomotionCode2
Theory of Generative Deep Learning : Probe Landscape of Empirical Error via Norm Based Capacity Control—0
Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution—0
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report—0
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation—0
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation NetworksCode0
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation—0
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution—0
Adversarial Audio Super-Resolution with Unsupervised Feature Losses—0
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields—0
Kernel based low-rank sparse model for single image super-resolution—0
Photometric Depth Super-ResolutionCode0
Multigrid Backprojection Super-Resolution and Deep Filter VisualizationCode0
Learning for Video Super-Resolution through HR Optical Flow EstimationCode1
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
The 2018 PIRM Challenge on Perceptual Image Super-resolutionCode1
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss—0
Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical RectificationCode0
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