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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Remote-Sensing Image Denoising With Multi-Sourced Information
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Remote-Sensing Image Denoising With Multi-Sourced Information

机译：具有多源信息的遥感图像去噪

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摘要

In this paper, a new multi-source-information-based method for remote-sensing image denoising is proposed. Different from the conventional denoising algorithm, the proposed method employs the features of similar reference images from the different band, different sensors, or from multi-temporal images when denoising a target image. The noise-free reference images as a prior is introduced into the denoising object function. The prior's information about the reference image is explored in two aspects: dictionary learning and edge-feature prediction. For dictionary learning, we improve the basis training process by incremental singular value decomposition. For edge-feature prediction, we construct the relationship between gradients of the target image and the reference image by linear ridge regression. The new denoising object function employs both the sparsity of the coefficient and the edge similarity between the target image and the reference image. We also present the optimization scheme for the proposed denoising model. Some typical cases based on different feature relations between a target image and a reference image are comprehensively discussed. Reasonably utilizing the similarity between the target image and the reference image, the proposed algorithm smooths out more noise and conserves more detail at the same time. Better performance of the proposed method is confirmed when compared with other state-of-the-art reference-based denoising methods.
机译：本文提出了一种新的基于多源信息的基于多源信息的遥感图像去噪方法。与传统的去噪算法不同，所提出的方法采用来自不同频带，不同传感器的类似参考图像的特征，或者在去噪到目标图像时从多时间图像。作为先前的无噪声参考图像被引入到去噪物功能中。有关参考图像的信息在两个方面探讨：字典学习和边缘特征预测。对于字典学习，我们通过增量奇异值分解来改善基础培训过程。对于边缘特征预测，通过线性脊回归构造目标图像的梯度与参考图像之间的关系。新的去噪对象函数采用系数的稀疏性和目标图像和参考图像之间的边缘相似度。我们还提出了拟议的去噪模式的优化方案。综合地讨论了基于目标图像和参考图像之间的不同特征关系的一些典型案例。合理利用目标图像和参考图像之间的相似性，所提出的算法平滑出更多的噪声并同时节省更多细节。与其他基于最先进的基于基于基于基于的去噪方法相比，确认了所提出的方法的更好性能。

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