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 2551–2560 of 3874 papers

TitleStatusHype
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
Optimal Surface Segmentation with Convex Priors in Irregularly Sampled Space—0
Optimal Transport driven CycleGAN for Unsupervised Learning in Inverse Problems—0
Optimal Transport for Super Resolution Applied to Astronomy Imaging—0
Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographics—0
Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified