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 2511–2520 of 3874 papers

TitleStatusHype
Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples—0
Characteristic Regularisation for Super-Resolving Face Images—0
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution—0
Omnidirectional Video Super-Resolution using Deep Learning—0
Omniscient Video Super-Resolution—0
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model—0
On Adapting Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction—0
On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit—0
On-Device Text Image Super Resolution—0
Channel Splitting Network for Single MR Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified