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 3711–3720 of 3874 papers

TitleStatusHype
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild—0
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network—0
Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction—0
Convolutional Low-Resolution Fine-Grained Classification—0
GUN: Gradual Upsampling Network for Single Image Super-Resolution—0
Local Patch Encoding-Based Method for Single Image Super-Resolution—0
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution—0
LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network—0
Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging—0
A recommender system to restore images with impulse noise—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified