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 2211–2220 of 3874 papers

TitleStatusHype
TRAMBA: A Hybrid Transformer and Mamba Architecture for Practical Audio and Bone Conduction Speech Super Resolution and Enhancement on Mobile and Wearable Platforms—0
Cross-Scale Residual Network for Multiple Tasks:Image Super-resolution, Denoising, and Deblocking—0
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images—0
Mamba-based Light Field Super-Resolution with Efficient Subspace Scanning—0
Cross-Modality High-Frequency Transformer for MR Image Super-Resolution—0
MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling—0
MambaLiteSR: Image Super-Resolution with Low-Rank Mamba using Knowledge Distillation—0
MAMBO: High-Resolution Generative Approach for Mammography Images—0
Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning—0
Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified