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 3781–3790 of 3874 papers

TitleStatusHype
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip ConnectionsCode0
FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos—0
Perceptual Losses for Real-Time Style Transfer and Super-ResolutionCode1
Global-Local Face Upsampling Network—0
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm—0
Con-Patch: When a Patch Meets its Context—0
Patch-Ordering as a Regularization for Inverse Problems in Image ProcessingCode0
Structured illumination microscopy image reconstruction algorithmCode0
Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization—0
Studying Very Low Resolution Recognition Using Deep Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified