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 1951–1960 of 3874 papers

TitleStatusHype
A GPU-Accelerated Light-field Super-resolution Framework Based on Mixed Noise Model and Weighted Regularization—0
A No-Reference Deep Learning Quality Assessment Method for Super-resolution Images Based on Frequency Maps—0
VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-ResolutionCode2
Volumetric Image Projection Super-Resolution Ultrasound (VIP-SR) with a 1D Unfocused Linear Array—0
Robust Deep Ensemble Method for Real-world Image DenoisingCode0
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution—0
Patch-based image Super Resolution using generalized Gaussian mixture model—0
Real-World Image Super-Resolution by Exclusionary Dual-LearningCode1
Recurrent Video Restoration Transformer with Guided Deformable AttentionCode1
Real-Time Super-Resolution for Real-World Images on Mobile Devices—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified