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 1191–1200 of 3874 papers

TitleStatusHype
BadRefSR: Backdoor Attacks Against Reference-based Image Super ResolutionCode0
MemNet: A Persistent Memory Network for Image RestorationCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse ProblemsCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
Deep Laplacian Pyramid Networks for Fast and Accurate Super-ResolutionCode0
Deep Laplacian Pyramid Network for Text Images Super-ResolutionCode0
Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionCode0
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through timeCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified