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 1891–1900 of 3874 papers

TitleStatusHype
IterInv: Iterative Inversion for Pixel-Level T2I ModelsCode0
SCAN-MUSIC: An Efficient Super-resolution Algorithm for Single Snapshot Wide-band Line Spectral Estimation—0
Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy—0
Blind Image Super-resolution with Rich Texture-Aware Codebooks—0
BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in Time-series Load Profiles—0
Local Statistics for Generative Image Detection—0
Single-pixel imaging based on deep learning—0
Spectral-based detection of chromatin loops in multiplexed super-resolution FISH data—0
Unpaired MRI Super Resolution with Contrastive Learning—0
A Coordinate Descent Approach to Atomic Norm Denoising—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified