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 3271–3280 of 3874 papers

TitleStatusHype
A learning-based view extrapolation method for axial super-resolution—0
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution—0
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser—0
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution—0
Stochastic Super-Resolution For Gaussian Textures—0
Stochastic super-resolution for Gaussian microtextures—0
Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models—0
3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution—0
Stop-and-go wave super-resolution reconstruction via iterative refinement—0
3D Super-Resolution Ultrasound with Adaptive Weight-Based Beamforming—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified