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 3621–3630 of 3874 papers

TitleStatusHype
Fast single image super-resolution based on sigmoid transformation—0
Fast Spatio-Temporal Residual Network for Video Super-Resolution—0
FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices with A Simple Super-Resolution Pipeline—0
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit—0
FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition—0
Advances on CNN-based super-resolution of Sentinel-2 images—0
Temporal shape super-resolution by intra-frame motion encoding using high-fps structured light—0
Temporal Super-Resolution using Multi-Channel Illumination Source—0
FDAN: Flow-guided Deformable Alignment Network for Video Super-Resolution—0
Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified