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 2941–2950 of 3874 papers

TitleStatusHype
Robust image reconstruction from multi-view measurements—0
Robust Multi-Image Based Blind Face Hallucination—0
Robust Online Video Super-Resolution Using an Efficient Alternating Projections Scheme—0
Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography—0
A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport—0
ASSR-NeRF: Arbitrary-Scale Super-Resolution on Voxel Grid for High-Quality Radiance Fields Reconstruction—0
Assessing Wireless Sensing Potential with Large Intelligent Surfaces—0
Robust Regression via Deep Negative Correlation Learning—0
A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT—0
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified