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 1061–1070 of 3874 papers

TitleStatusHype
Near field Acoustic Holography on arbitrary shapes using Convolutional Neural NetworkCode0
Neural Architecture Search for Deep Image PriorCode0
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold DiscriminationCode0
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-ResolutionCode0
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object RepresentationCode0
Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super ResolutionCode0
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 imagesCode0
Multi-scale deep neural networks for real image super-resolutionCode0
Multi-scale Residual Network for Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified