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 1301–1350 of 3874 papers

TitleStatusHype
A Generative Diffusion Model to Solve Inverse Problems for Robust in-NICU Neonatal MRI—0
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Generative Adversarial Models for Extreme Geospatial Downscaling—0
Deep Learning Based Autonomous Vehicle Super Resolution DOA Estimation for Safety Driving—0
Deep learning at scale for subgrid modeling in turbulent flows—0
AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices—0
Generative adversarial network for super-resolution imaging through a fiber—0
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution—0
Deep Learning-Assisted Simultaneous Targets Sensing and Super-Resolution Imaging—0
Back-Projection Pipeline—0
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks—0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results—0
2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts—0
Backdoor Attacks against Image-to-Image Networks—0
Generating Unobserved Alternatives—0
Generative Adversarial Classifier for Handwriting Characters Super-Resolution—0
Generative Adversarial Networks for Image Super-Resolution: A Survey—0
A Generative Model for Generic Light Field Reconstruction—0
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution—0
“Zero-Shot” Super-Resolution Using Deep Internal Learning—0
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution—0
AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content—0
Deep Image Super Resolution via Natural Image Priors—0
Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy—0
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm—0
A Wideband Distributed Massive MIMO Channel Sounder for Communication and Sensing—0
Deep Hierarchical Super Resolution for Scientific Data—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
Deep generative model super-resolves spatially correlated multiregional climate data—0
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging—0
Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations—0
Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study—0
AIM 2020 Challenge on Video Temporal Super-Resolution—0
Auxiliary Features-Guided Super Resolution for Monte Carlo Rendering—0
Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
Deep filter bank regression for super-resolution of anisotropic MR brain images—0
Deep EEG Super-Resolution: Upsampling EEG Spatial Resolution with Generative Adversarial Networks—0
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution—0
Automated Symbolic Law Discovery: A Computer Vision Approach—0
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results—0
Generalized Matrix-Pencil Approach to Estimation of Complex Exponentials with Gapped Data—0
OFDM Reference Signal Pattern Design Criteria for Integrated Communication and Sensing—0
DeepDPM: Dynamic Population Mapping via Deep Neural Network—0
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network—0
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results—0
Generalizable One-shot Neural Head Avatar—0
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified