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 741–750 of 3874 papers

TitleStatusHype
SpikeMM: Flexi-Magnification of High-Speed Micro-Motions—0
Climate Variable Downscaling with Conditional Normalizing Flows—0
All-In-One Medical Image Restoration via Task-Adaptive RoutingCode2
Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?—0
Blind Image Restoration via Fast Diffusion InversionCode3
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory MatchingCode2
Single image super-resolution based on trainable feature matching attention networkCode0
Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search—0
Hyperspectral and multispectral image fusion with arbitrary resolution through self-supervised representationsCode0
Towards a Sampling Theory for Implicit Neural Representations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified