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 2911–2920 of 3874 papers

TitleStatusHype
Interpretable Super-Resolution via a Learned Time-Series Representation—0
Super-resolution Variational Auto-EncodersCode1
Neural Sparse Representation for Image RestorationCode1
Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression—0
Learning Texture Transformer Network for Image Super-ResolutionCode1
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution—0
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars MiningCode1
Robust Reference-Based Super-Resolution With Similarity-Aware Deformable ConvolutionCode1
Unsupervised Adaptation Learning for Hyperspectral Imagery Super-ResolutionCode1
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified