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 791–800 of 3874 papers

TitleStatusHype
Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse ArraysCode1
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
I^3Net: Inter-Intra-slice Interpolation Network for Medical Slice SynthesisCode0
Self-Supervised Learning for Real-World Super-Resolution from Dual and Multiple Zoomed ObservationsCode2
Single Image Super-Resolution Based on Global-Local Information Synergy—0
TRAMBA: A Hybrid Transformer and Mamba Architecture for Practical Audio and Bone Conduction Speech Super Resolution and Enhancement on Mobile and Wearable Platforms—0
Reference-Free Image Quality Metric for Degradation and Reconstruction Artifacts—0
Detail-Enhancing Framework for Reference-Based Image Super-Resolution—0
Towards Real-world Video Face Restoration: A New Benchmark—0
Swin2-MoSE: A New Single Image Super-Resolution Model for Remote SensingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified