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 2301–2350 of 3874 papers

TitleStatusHype
Mobile Computational Photography: A Tour—0
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention—0
Translation position extracting in incoherent Fourier ptychography—0
Model Adaptation for Inverse Problems in Imaging—0
Content-adaptive Representation Learning for Fast Image Super-resolution—0
Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization—0
Model-Driven Channel Estimation for OFDM Systems Based on Image Super- Resolution Network—0
Constrained Diffusion Implicit Models—0
A Comprehensive Comparison of Projections in Omnidirectional Super-Resolution—0
Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement—0
Modeling Deformable Gradient Compositions for Single-Image Super-Resolution—0
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images—0
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution—0
Modelling Point Spread Function in Fluorescence Microscopy with a Sparse Combination of Gaussian Mixture: Trade-off between Accuracy and Efficiency—0
Model reduction for the material point method via an implicit neural representation of the deformation map—0
A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite Data—0
Consistency Trajectory Matching for One-Step Generative Super-Resolution—0
ConsisSR: Delving Deep into Consistency in Diffusion-based Image Super-Resolution—0
Monitoring of Hermit Crabs Using drone-captured imagery and Deep Learning based Super-Resolution Reconstruction and Improved YOLOv8—0
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution—0
More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination—0
Morphological Reconstruction Improves Microvessel Mapping in Super-Resolution Ultrasound—0
Mosaic Super-resolution via Sequential Feature Pyramid Networks—0
Con-Patch: When a Patch Meets its Context—0
CoNO: Complex Neural Operator for Continous Dynamical Physical Systems—0
Motion Guided LIDAR-camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles—0
Transport-based analysis, modeling, and learning from signal and data distributions—0
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution—0
MPSI: Mamba enhancement model for pixel-wise sequential interaction Image Super-Resolution—0
Transport-Based Single Frame Super Resolution of Very Low Resolution Face Images—0
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion—0
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network—0
MR imaging in the low-field: Leveraging the power of machine learning—0
Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution—0
Transthoracic super-resolution ultrasound localisation microscopy of myocardial vasculature in patients—0
MRI Super-Resolution with Ensemble Learning and Complementary Priors—0
MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better—0
MrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution—0
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution—0
MSCE: An edge preserving robust loss function for improving super-resolution algorithms—0
MSCrackMamba: Leveraging Vision Mamba for Crack Detection in Fused Multispectral Imagery—0
MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution—0
TriNeRFLet: A Wavelet Based Triplane NeRF Representation—0
MSRA-SR: Image Super-resolution Transformer with Multi-scale Shared Representation Acquisition—0
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution—0
Conditioning 3D Diffusion Models with 2D Images: Towards Standardized OCT Volumes through En Face-Informed Super-Resolution—0
Conditioned Regression Models for Non-Blind Single Image Super-Resolution—0
Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution—0
Multi-Attributed and Structured Text-to-Face Synthesis—0
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified