Abstract
Synthetic aperture radar (SAR) image despeckling is an essential problem in remote sensing technology, which has a strong influence on the performance of the following processing. We propose a new despeckled algorithm combining CNN and fractional-order total variation. Through constructing a CNN model and introducing the fractional-order total variation into loss function as the regularization term, the experimental results prove that our proposed method can avoid detail ambiguity and overly smooth caused by integral-order, and preserve rich texture and details information. Therefore, the high-quality despeckled images generated by our model will significantly improve the availability of SAR images.
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Bai, YC., Zhang, S., Chen, M., Pu, YF., Zhou, JL. (2018). A Fractional Total Variational CNN Approach for SAR Image Despeckling. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_46
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