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 2881–2890 of 3874 papers

TitleStatusHype
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling—0
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation—0
A unified method for super-resolution recovery and real exponential-sum separation—0
Residual Feature Aggregation Network for Image Super-Resolution—0
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions—0
Residual learning based densely connected deep dilated network for joint deblocking and super resolution—0
Residual Learning Inspired Crossover Operator and Strategy Enhancements for Evolutionary Multitasking—0
Augmenting Perceptual Super-Resolution via Image Quality Predictors—0
Residual Networks for Light Field Image Super-Resolution—0
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified