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 171–180 of 3874 papers

TitleStatusHype
Image Super-Resolution Using Very Deep Residual Channel Attention NetworksCode2
Immersive Neural Graphics PrimitivesCode2
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
CMGAN: Conformer-Based Metric-GAN for Monaural Speech EnhancementCode2
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel FusionCode2
Latent Modulated Function for Computational Optimal Continuous Image RepresentationCode2
Learning Generative Structure Prior for Blind Text Image Super-resolutionCode2
Learning Trajectory-Aware Transformer for Video Super-ResolutionCode2
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differencesCode2
Boosting Flow-based Generative Super-Resolution Models via Learned PriorCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified