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 1251–1260 of 3874 papers

TitleStatusHype
Deeply Aggregated Alternating Minimization for Image Restoration—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels—0
Benchmarking Super-Resolution Algorithms on Real Data—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100 speed-up—0
Deep Learning Techniques for Super-Resolution in Video Games—0
Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis—0
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging—0
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified