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 3521–3530 of 3874 papers

TitleStatusHype
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)—0
Deep Learning for Single Image Super-Resolution: A Brief ReviewCode0
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging—0
Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer LearningCode0
To learn image super-resolution, use a GAN to learn how to do image degradation firstCode0
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks—0
CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale WarpingCode0
Gated Fusion Network for Joint Image Deblurring and Super-ResolutionCode0
A Tensor Factorization Method for 3D Super-Resolution with Application to Dental CTCode0
Decouple Learning for Parameterized Image OperatorsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified