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 701–710 of 3874 papers

TitleStatusHype
Image Super-Resolution Using Deep Convolutional NetworksCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Diffusion Models Beat GANs on Image ClassificationCode1
DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion ModelsCode1
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-ResolutionCode1
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in ImagingCode1
A new public Alsat-2B dataset for single-image super-resolutionCode1
DeblurSR: Event-Based Motion Deblurring Under the Spiking RepresentationCode1
Implicit Grid Convolution for Multi-Scale Image Super-ResolutionCode1
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified