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 1321–1330 of 3874 papers

TitleStatusHype
Explorable Super ResolutionCode0
A Comprehensive End-to-End Computer Vision Framework for Restoration and Recognition of Low-Quality Engineering DrawingsCode0
Learning of Patch-Based Smooth-Plus-Sparse Models for Image ReconstructionCode0
Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super ResolutionCode0
A Regularized Conditional GAN for Posterior Sampling in Image Recovery ProblemsCode0
Exemplar Guided Face Image Super-Resolution without Facial LandmarksCode0
Learning Parallax Attention for Stereo Image Super-ResolutionCode0
Conditional Generation Using Polynomial ExpansionsCode0
Light Field Super-resolution via Attention-Guided Fusion of Hybrid LensesCode0
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution ForestsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified