Abstract Deep learning denoising models can automatically extract underwater heterogeneous information data features to improve denoising performance through an internal network structure. In the present study, a novel stacked convolutional sparse denoising autoencoder (SCSDA) model was proposed in this paper to complete the blind denoising task of underwater heterogeneous information data. Specifically, for the first time, the stacked sparse denoising autoencoder (SSDA) was constructed by three sparse denoising autoencoders (SDA) to extract overcomplete sparse features. Then, the output of the last encoding layer of the SSDA was used as the input of the convolutional neural network (CNN) to further extract the deep features. Finally, in order to solve the lack of the pure underwater heterogenous information data during the acquisition and transmission process, the unrelated dataset was developed to simulate the underwater heterogeneous information data as the training set in proposed SCSDA model. Compared with the existing other algorithms, the experiment results demonstrate that the proposed SCSDA model combines the advantages of SSDA and CNN, which has great blind denoising ability. It can process faster and preserves more edge features of underwater heterogeneous information data. Also, it has a certain degree of robustness and effectiveness.

中文翻译：

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一种用于水下异构信息数据的堆叠卷积稀疏去噪自编码器模型

摘要 深度学习去噪模型可以通过内部网络结构自动提取水下异构信息数据特征以提高去噪性能。在本研究中，本文提出了一种新颖的堆叠卷积稀疏自编码器（SCSDA）模型来完成水下异构信息数据的盲去噪任务。具体而言，首次由三个稀疏去噪自编码器（SDA）构建堆叠稀疏去噪自编码器（SSDA）以提取过完备的稀疏特征。然后，将SSDA最后一个编码层的输出作为卷积神经网络（CNN）的输入，进一步提取深层特征。最后，为了解决采集和传输过程中缺乏纯水下异构信息数据的问题，开发了无关数据集来模拟水下异构信息数据作为所提出的SCSDA模型中的训练集。与现有的其他算法相比，实验结果表明，所提出的SCSDA模型结合了SSDA和CNN的优点，具有很强的盲去噪能力。它可以更快地处理并保留更多水下异构信息数据的边缘特征。此外，它具有一定的稳健性和有效性。实验结果表明，所提出的SCSDA模型结合了SSDA和CNN的优点，具有很强的盲去噪能力。它可以更快地处理并保留水下异构信息数据的更多边缘特征。此外，它具有一定的稳健性和有效性。实验结果表明，所提出的SCSDA模型结合了SSDA和CNN的优点，具有很强的盲去噪能力。它可以更快地处理并保留更多水下异构信息数据的边缘特征。此外，它具有一定的稳健性和有效性。