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 3551–3560 of 3874 papers

TitleStatusHype
Superresolution method for data deconvolution by superposition of point sources—0
Image Super-Resolution via Dual-State Recurrent NetworksCode0
Large Receptive Field Networks for High-Scale Image Super-Resolution—0
Super-resolution Ultrasound Localization Microscopy through Deep Learning—0
Densely Connected High Order Residual Network for Single Frame Image Super Resolution—0
Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach—0
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution—0
An efficient CNN for spectral reconstruction from RGB imagesCode1
A two-stage 3D Unet framework for multi-class segmentation on full resolution image—0
Reference-Conditioned Super-Resolution by Neural Texture Transfer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified