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 2531–2540 of 3874 papers

TitleStatusHype
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning—0
Signal reconstruction via operator guiding—0
SIGNET: Efficient Neural Representation for Light Fields—0
SimDA: Simple Diffusion Adapter for Efficient Video Generation—0
Similarity-Aware Patchwork Assembly for Depth Image Super-Resolution—0
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution—0
Simple and Efficient Unpaired Real-world Super-Resolution using Image Statistics—0
SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data—0
Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction—0
Simulation-informed deep learning for enhanced SWOT observations of fine-scale ocean dynamics—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified