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 2821–2830 of 3874 papers

TitleStatusHype
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified