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 851–860 of 3874 papers

TitleStatusHype
Memory-Efficient Hierarchical Neural Architecture Search for Image RestorationCode1
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrastCode1
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution NetworksCode1
Neural Radiance Flow for 4D View Synthesis and Video ProcessingCode1
Zoom-to-Inpaint: Image Inpainting with High-Frequency DetailsCode1
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local AdjustmentCode1
Improving the Fairness of Deep Generative Models without RetrainingCode1
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual NetworkCode1
Bayesian Image Reconstruction using Deep Generative ModelsCode1
Hierarchical Residual Attention Network for Single Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified