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 3226–3250 of 3874 papers

TitleStatusHype
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report—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
Real-World Super-Resolution of Face-Images from Surveillance Cameras—0
Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling—0
ReCamMaster: Camera-Controlled Generative Rendering from A Single Video—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
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
Recurrent Structure Attention Guidance for Depth Super-Resolution—0
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data—0
Recursive Deep Prior Video: a Super Resolution algorithm for Time-Lapse Microscopy of organ-on-chip experiments—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
Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation—0
Reference-Conditioned Super-Resolution by Neural Texture Transfer—0
Reference-Free Image Quality Metric for Degradation and Reconstruction Artifacts—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified