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 2401–2410 of 3874 papers

TitleStatusHype
Compressed Ultrasound Imaging:from Sub-Nyquist Rates to Super-Resolution—0
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data—0
Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal Image Super Resolution—0
Multi-scale Image Super Resolution with a Single Auto-Regressive Model—0
Multi-Scale Implicit Transformer with Re-parameterize for Arbitrary-Scale Super-Resolution—0
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution—0
Turning Frequency to Resolution: Video Super-Resolution via Event Cameras—0
TV-based Deep 3D Self Super-Resolution for fMRI—0
Multi-scale Super-resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness—0
Multispectral Compressive Imaging Strategies using Fabry-Pérot Filtered Sensors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified