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
Dense xUnit NetworksCode0
Deep Laplacian Pyramid Network for Text Images Super-ResolutionCode0
Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images—0
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement—0
Three-dimensional Optical Coherence Tomography Image Denoising through Multi-input Fully-Convolutional Networks—0
NeuroTreeNet: A New Method to Explore Horizontal Expansion Network—0
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network—0
Low-Resolution Face Recognition—0
Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound—0
Adversarial Feedback LoopCode0
Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery—0
Single Snapshot Super-Resolution DOA Estimation for Arbitrary Array Geometries—0
A Learning-Based Framework for Line-Spectra Super-resolutionCode0
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering—0
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution NetworkCode0
Functional Nonlinear Sparse Models—0
Bi-GANs-ST for Perceptual Image Super-resolution—0
Neural Nearest Neighbors NetworksCode0
DeepDPM: Dynamic Population Mapping via Deep Neural Network—0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution—0
Channel Splitting Network for Single MR Image Super-Resolution—0
Deep learning-based super-resolution in coherent imaging systems—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
MRI Super-Resolution using Multi-Channel Total VariationCode0
Triple Attention Mixed Link Network for Single Image Super Resolution—0
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation—0
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
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
Kernel based low-rank sparse model 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
Photometric Depth Super-ResolutionCode0
Multigrid Backprojection Super-Resolution and Deep Filter VisualizationCode0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
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
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual QualityCode0
Generative adversarial network-based image super-resolution using perceptual content lossesCode0
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution—0
Deep MR Image Super-Resolution Using Structural Priors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified