Haidong Ding
Dalian Institute of Chemical Physics
7 Papers
Haidong Ding is an academic researcher from Dalian Institute of Chemical Physics. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 1, co-authored 1 publications.
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Papers
Uncertainty-Based Thin Cloud Removal Network via Conditional Variational Autoencoders
Haidong Ding,Yue Zi,Fengying Xie +2 more
TL;DR: Ding et al. as mentioned in this paper proposed a thin cloud removal network via conditional variational autoencoders (CVAE) to generate multiple reasonable cloud-free images for each input cloud image.
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Wavelet Integrated Convolutional Neural Network for Thin Cloud Removal in Remote Sensing Images
TL;DR: Wang et al. as discussed by the authors proposed a novel wavelet-integrated convolutional neural network for thin cloud removal (WaveCNN-CR) in remote sensing images that can obtain larger receptive fields without any information loss.
Skin lesion classification based on two-modal images using a multi-scale fully-shared fusion network
TL;DR: Wang et al. as mentioned in this paper proposed a multi-scale Fully-shared Fusion Network (MFF-Net) that combines deep and shallow features within individual modalities to reduce the loss of spatial information in high-level feature maps.
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Robust Haze and Thin Cloud Removal via Conditional Variational Autoencoders
Haidong Ding,Fengying Xie,Linwei Qiu,Xiaozhe Zhang,Zhen Xia Shi +4 more
TL;DR: A novel algorithm for haze and thin cloud removal using conditional variational autoencoders (CVAEs) to generate multiple realistic restored images for each input and a dynamic fusion network (DFN) for combining multiple plausible outcomes to obtain a more accurate result.
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Feedback Network for Compact Thin Cloud Removal
TL;DR: Wang et al. as discussed by the authors proposed a compact thin cloud removal network based on the feedback mechanism, called CRFB-Net, which leverages the high-level features as feedback information to modulate shallow representations.
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