Yanting Hu
Xinjiang Medical University
10 Papers
8 Citations
Yanting Hu is an academic researcher from Xinjiang Medical University. The author has contributed to research in topics: Computer science & Feature (computer vision). The author has an hindex of 5, co-authored 7 publications. Previous affiliations of Yanting Hu include Xidian University.
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Papers
Channel-Wise and Spatial Feature Modulation Network for Single Image Super-Resolution
TL;DR: A channel-wise and spatial feature modulation (CSFM) network in which a series of feature modulation memory (FMM) modules are cascaded with a densely connected structure to transform shallow features to high informative features and maintain long-term information for image super-resolution.
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Single Image Super-Resolution via Cascaded Multi-Scale Cross Network.
TL;DR: This work proposes a cascaded multi-scale cross network (CMSC) in which a sequence of subnetworks is cascaded to infer high resolution features in a coarse-to-fine manner and introduces residual-features learning in each stage to boost reconstruction performance.
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Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution.
TL;DR: A purposeful and interpretable detail-fidelity attention network to progressively process these smoothes and details in a divide-and-conquer manner, which is a novel and specific prospect of image super-resolution for the purpose of improving detail fidelity.
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Single image super-resolution with multi-scale information cross-fusion network
TL;DR: A multi-scale information cross-fusion network (MSICF) in which a sequence of subnetworks is cascaded to infer high resolution features in a coarse-to-fine manner is proposed to improve information flow and capture sufficient knowledge for reconstructing high-frequency details.
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Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution
TL;DR: Huang et al. as mentioned in this paper proposed a Hessian filter for interpretable high-profile feature representation for detail inference, along with a dilated encoder-decoder and a distribution alignment cell to improve the inferred Hessian features in a morphological manner and statistical manner respectively.
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