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 2111–2120 of 3874 papers

TitleStatusHype
Learning Two-factor Representation for Magnetic Resonance Image Super-resolution—0
Learning with Privileged Information for Efficient Image Super-Resolution—0
Depth-Independent Depth Completion via Least Square Estimation—0
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow—0
LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network—0
Lessons Learned Report: Super-Resolution for Detection Tasks in Engineering Problem-Solving—0
Level generation and style enhancement -- deep learning for game development overview—0
Leveraging Land Cover Priors for Isoprene Emission Super-Resolution—0
Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution—0
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified