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 2321–2330 of 3874 papers

TitleStatusHype
More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination—0
Morphological Reconstruction Improves Microvessel Mapping in Super-Resolution Ultrasound—0
Mosaic Super-resolution via Sequential Feature Pyramid Networks—0
Con-Patch: When a Patch Meets its Context—0
CoNO: Complex Neural Operator for Continous Dynamical Physical Systems—0
Motion Guided LIDAR-camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles—0
Transport-based analysis, modeling, and learning from signal and data distributions—0
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution—0
MPSI: Mamba enhancement model for pixel-wise sequential interaction Image Super-Resolution—0
Transport-Based Single Frame Super Resolution of Very Low Resolution Face Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified