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 861–870 of 3874 papers

TitleStatusHype
Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape Reconstruction from Point CloudsCode1
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
Bayesian Image Reconstruction using Deep Generative ModelsCode1
Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolutionCode1
Bayesian Image Super-Resolution with Deep Modeling of Image StatisticsCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Dynamic Implicit Image Function for Efficient Arbitrary-Scale Image RepresentationCode1
PUGeo-Net: A Geometry-centric Network for 3D Point Cloud UpsamplingCode1
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-ResolutionCode1
DynaVSR: Dynamic Adaptive Blind Video Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified