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 1051–1060 of 3874 papers

TitleStatusHype
Iterative Token Evaluation and Refinement for Real-World Super-ResolutionCode1
AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution—0
Kandinsky 3.0 Technical ReportCode2
Training Neural Networks on RAW and HDR Images for Restoration TasksCode1
J-Net: Improved U-Net for Terahertz Image Super-Resolution—0
Conditional Variational Diffusion ModelsCode1
TMSR: Tiny Multi-path CNNs for Super Resolution—0
Generative Powers of Ten—0
SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network—0
ESTformer: Transformer Utilizing Spatiotemporal Dependencies for Electroencaphalogram Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified