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 2571–2580 of 3874 papers

TitleStatusHype
Single-Layer Learnable Activation for Implicit Neural Representation (SL^2A-INR)—0
Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network—0
Single particle algorithms to reveal cellular nanodomain organization—0
Single-photon Image Super-resolution via Self-supervised Learning—0
Single-pixel imaging based on deep learning—0
Single-sample image-fusion upsampling of fluorescence lifetime images—0
Single-shot structured illumination microscopy—0
Single-snapshot machine learning for super-resolution of turbulence—0
Single Snapshot Super-Resolution DOA Estimation for Arbitrary Array Geometries—0
Single-Step Latent Consistency Model for Remote Sensing Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified