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 3041–3050 of 3874 papers

TitleStatusHype
Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression—0
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution—0
Inter-Task Association Critic for Cross-Resolution Person Re-Identification—0
Residual Feature Aggregation Network for Image Super-Resolution—0
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis—0
Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges—0
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks—0
Zoom in to the details of human-centric videos—0
Bayesian Conditional GAN for MRI Brain Image Synthesis—0
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified