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 2241–2250 of 3874 papers

TitleStatusHype
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems—0
Transformation Consistency Regularization – A Semi-Supervised Paradigm for Image-to-Image Translation—0
Medical image super-resolution method based on dense blended attention network—0
Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution—0
Coupled-Projection Residual Network for MRI Super-Resolution—0
CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution—0
Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition—0
MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution—0
Cost-effective photonic super-resolution millimeter-wave joint radar-communication system using self-coherent detection—0
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified