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 1241–1250 of 3874 papers

TitleStatusHype
Deep multi-frame face super-resolution—0
Deep MR Image Super-Resolution Using Structural Priors—0
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors—0
BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in Time-series Load Profiles—0
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—0
Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging—0
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis—0
Benefiting from Multitask Learning to Improve Single Image Super-Resolution—0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified