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 691–700 of 3874 papers

TitleStatusHype
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-ResolutionCode1
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-ResolutionCode1
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
Hyperspectral Image Super Resolution with Real Unaligned RGB GuidanceCode1
DL4DS -- Deep Learning for empirical DownScalingCode1
CoReFusion: Contrastive Regularized Fusion for Guided Thermal Super-ResolutionCode1
DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion ModelsCode1
ICME 2025 Grand Challenge on Video Super-Resolution for Video ConferencingCode1
Diffusion-based Blind Text Image Super-ResolutionCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified