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Hierarchical contrastive learning and color standardization for single image sand-dust removal

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Abstract

Convolutional neural networks (CNN) have demonstrated impressive performance in reconstructing images in challenging environments. However, there is still a blank in the field of CNN-based sandstorm image processing. Existing sandstorm removal algorithms enhance degraded images by using prior knowledge, but often fail to address the issues of color cast, low contrast, and poor recognizability. To bridge the gap, we present a novel end-to-end sand-dust reconstruction network and incorporate hierarchical contrastive regularization and color constraint in the network. Based on contrastive learning, the hierarchical contrastive regularization reconstructs the sand-free image by pulling it closer to ’positive’ pairs while pushing it away from ’negative’ pairs in representation space. Furthermore, considering the specific characteristics of sandstorm images, we introduce the color constraint term as a sub-loss function to balance the hue, saturation, and value of the reconstructed image. Experimental results show that the proposed SdR-Net outperforms state-of-the-arts in both quantitative and qualitative.

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Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

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Funding

This work is supported by the National Key Research and Development Program of China (2019YFB1312102) and the Natural Science Foundation of Hebei Province (F2019202364).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China

    Yazhong Si & Fan Yang

  2. School of NARI Electrical and Automation, Nanjing Normal University, Nanjing, 210023, China

    Mengjia Xu

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  1. Yazhong Si
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  2. Mengjia Xu
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  3. Fan Yang
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Correspondence to Fan Yang.

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Si, Y., Xu, M. & Yang, F. Hierarchical contrastive learning and color standardization for single image sand-dust removal. Pattern Anal Applic 27, 5 (2024). https://doi.org/10.1007/s10044-024-01231-w

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