Stripe noise from different remote sensing imaging systems varies considerably in terms of response, length, angle, and periodicity. Due to the complex distributions of different stripes, the destriping results of previous methods may be oversmoothed or contain residual stripe. To overcome this key problem, we provide a comprehensive analysis of existing destriping methods and propose a deep convolutional neural network (CNN) for handling various kinds of stripes. Moreover, previous methods individually model the stripe or the image priors, which may lose the relationship between them. In this article, a two-stream CNN is designed to simultaneously model the stripe and image, which better facilitates distinguishing them from each other. Moreover, we incorporate the wavelet into our CNN model for better directional feature representation. Therefore, the CNN learns the discriminative representation from the external data set, while the wavelet models the internal directionality of the stripe, in which both the internal and external priors are beneficial to the destriping task. In addition, the wavelet extracts the multiscale information with a larger receptive field for global contextual information modeling; thus, we can better distinguish the stripe from the similar image line pattern structures. The proposed method has been extensively evaluated on a number of data sets and outperforms the state-of-the-art methods by substantially a large margin in terms of quantitative and qualitative assessments, speed, and robustness.

中文翻译：

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通过基于小波的深度卷积神经网络实现通用条纹去除

来自不同遥感成像系统的条纹噪声在响应、长度、角度和周期性方面差异很大。由于不同条纹的复杂分布，以前方法的去条纹结果可能会过度平滑或包含残留条纹。为了克服这个关键问题，我们对现有的去条纹方法进行了全面分析，并提出了一种用于处理各种条纹的深度卷积神经网络 (CNN)。此外，以前的方法单独对条纹或图像先验进行建模，这可能会失去它们之间的关系。在本文中，设计了一个双流 CNN 来同时对条纹和图像进行建模，从而更好地区分它们。此外，我们将小波合并到我们的 CNN 模型中以获得更好的方向特征表示。所以，CNN 从外部数据集中学习判别性表示，而小波对条纹的内部方向性进行建模，其中内部和外部先验都对去条纹任务有利。此外，小波提取感受野更大的多尺度信息进行全局上下文信息建模；因此，我们可以更好地将条纹与相似的图像线条图案结构区分开来。所提出的方法已在许多数据集上进行了广泛的评估，并且在定量和定性评估、速度和稳健性方面大大优于最先进的方法。其中内部和外部先验都有益于去条纹任务。此外，小波提取感受野更大的多尺度信息进行全局上下文信息建模；因此，我们可以更好地将条纹与相似的图像线条图案结构区分开来。所提出的方法已在许多数据集上进行了广泛的评估，并且在定量和定性评估、速度和稳健性方面大大优于最先进的方法。其中内部和外部先验都有益于去条任务。此外，小波提取感受野更大的多尺度信息进行全局上下文信息建模；因此，我们可以更好地将条纹与相似的图像线条图案结构区分开来。所提出的方法已在许多数据集上进行了广泛的评估，并且在定量和定性评估、速度和稳健性方面大大优于最先进的方法。