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.

中文翻译：

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单图像除尘的分层对比学习和颜色标准化

摘要

卷积神经网络 (CNN) 在具有挑战性的环境中重建图像方面表现出了令人印象深刻的性能。然而基于CNN的沙尘暴图像处理领域仍存在空白。现有的沙尘暴去除算法利用先验知识来增强退化图像，但往往无法解决偏色、对比度低和可识别性差的问题。为了弥补这一差距，我们提出了一种新颖的端到端沙尘重建网络，并在网络中结合了分层对比正则化和颜色约束。基于对比学习，分层对比正则化通过将无沙图像拉近表示空间中的“正”对，同时将其远离“负”对来重建无沙图像。此外，考虑到沙尘暴图像的具体特征，我们引入颜色约束项作为子损失函数来平衡重建图像的色调、饱和度和明度。实验结果表明，所提出的 SdR-Net 在定量和定性方面都优于最先进的技术。