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 3761–3770 of 3874 papers

TitleStatusHype
A Deep Primal-Dual Network for Guided Depth Super-Resolution—0
ATGV-Net: Accurate Depth Super-Resolution—0
Generic 3D Convolutional Fusion for image restoration—0
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks—0
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning—0
Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling—0
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network—0
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Scalable image coding based on epitomes—0
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified