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 551–560 of 3874 papers

TitleStatusHype
Cross-receptive Focused Inference Network for Lightweight Image Super-ResolutionCode1
Deep Adaptive Inference Networks for Single Image Super-ResolutionCode1
A Systematic Survey of Deep Learning-based Single-Image Super-ResolutionCode1
GAN Prior based Null-Space Learning for Consistent Super-ResolutionCode1
CoReFusion: Contrastive Regularized Fusion for Guided Thermal Super-ResolutionCode1
Flexible Style Image Super-Resolution using Conditional ObjectiveCode1
Flow-based Kernel Prior with Application to Blind Super-ResolutionCode1
Forecasting Tropical Cyclones with Cascaded Diffusion ModelsCode1
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified