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 2801–2850 of 3874 papers

TitleStatusHype
Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution—0
Real-Time Video Super-Resolution by Joint Local Inference and Global Parameter Estimation—0
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report—0
Benchmarking Super-Resolution Algorithms on Real Data—0
Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis—0
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network—0
Real-World Single Image Super-Resolution Under Rainy Condition—0
Bayesian Sparse Representation for Hyperspectral Image Super Resolution—0
Real-World Super-Resolution of Face-Images from Surveillance Cameras—0
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution—0
Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling—0
Bayesian Conditioned Diffusion Models for Inverse Problems—0
ReCamMaster: Camera-Controlled Generative Rendering from A Single Video—0
Bayesian Conditional GAN for MRI Brain Image Synthesis—0
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution—0
Reconfigurable AI Modules Aided Channel Estimation and MIMO Detection—0
Reconstruct Anything Model: a lightweight foundation model for computational imaging—0
Bayesian Based Unrolling for Reconstruction and Super-resolution of Single-Photon Lidar Systems—0
Reconstruct high-resolution multi-focal plane images from a single 2D wide field image—0
Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks—0
Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search—0
Reconstructing the Noise Manifold for Image Denoising—0
Reconstructing Three-decade Global Fine-Grained Nighttime Light Observations by a New Super-Resolution Framework—0
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement—0
Unsupervised Learning for Real-World Super-Resolution—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
Unsupervised Learning of High-resolution Light Field Imaging via Beam Splitter-based Hybrid Lenses—0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
Recurrent Structure Attention Guidance for Depth Super-Resolution—0
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data—0
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Back-Projection Pipeline—0
Recursive Deep Prior Video: a Super Resolution algorithm for Time-Lapse Microscopy of organ-on-chip experiments—0
Backdoor Attacks against Image-to-Image Networks—0
Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance—0
Redefining Neural Operators in d+1 Dimensions—0
Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations—0
Reducing Magnetic Resonance Image Spacing by Learning Without Ground-Truth—0
“Zero-Shot” Super-Resolution Using Deep Internal Learning—0
Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy—0
Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation—0
Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss—0
A Wideband Distributed Massive MIMO Channel Sounder for Communication and Sensing—0
Reference-Conditioned Super-Resolution by Neural Texture Transfer—0
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors—0
Reference-Free Image Quality Metric for Degradation and Reconstruction Artifacts—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
Unsupervised Projection Networks for Generative Adversarial Networks—0
Auxiliary Features-Guided Super Resolution for Monte Carlo Rendering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified