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 3081–3090 of 3874 papers

TitleStatusHype
On the Use of Singular Value Decomposition as a Clutter Filter for Ultrasound Flow Imaging—0
On training deep networks for satellite image super-resolution—0
On Versatile Video Coding at UHD with Machine-Learning-Based Super-Resolution—0
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution—0
OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution—0
Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation—0
Optical Flow for Video Super-Resolution: A Survey—0
Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolution—0
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network—0
Optimal Physical Preprocessing for Example-Based Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified