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 2731–2740 of 3874 papers

TitleStatusHype
DynaVSR: Dynamic Adaptive Blind Video Super-ResolutionCode1
Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution—0
Symmetric Parallax Attention for Stereo Image Super-ResolutionCode1
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction—0
Super-Resolution of Real-World Faces—0
Physics-Informed Neural Network Super Resolution for Advection-Diffusion Models—0
Solving Inverse Problems with Hybrid Deep Image Priors: the challenge of preventing overfittingCode1
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix—0
Generating Unobserved Alternatives—0
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image RestorationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified