SAR图像分析利用双边滤波器和前馈神经网络去斑

M. Kalaiyarasi, Swaminathan Saravanan, Bharath Kumar Narukullapati, I. Kasireddy, D. S. Naga Malleswara Rao, D. Nagineni Venkata Sireesha
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引用次数: 0

摘要

散斑噪声降低了SAR图像的质量和性质,降低了SAR图像处理的性能。因此,在利用不同的图像处理系统处理图像之前,必须抑制乘性噪声。尽管有许多可用的散斑降噪技术,但它们都有自己的优点和缺点。因此,降噪仍然是SAR图像处理的主要障碍。本文采用神经网络和双边滤波相结合的方法对图像的散斑噪声进行了抑制。本文还比较分析了两种分层FFBPNN, TLFFBPNN和FLFFBPNN对SAR图像散斑降噪的效果。通过比较,可以得出结论,TLFFBPNN去斑点方法具有较好的SN约简视觉效果,具有较好的相似性和边缘守恒指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of SAR ImagesDe-speckling using a Bilateral filter and Feed Forward Neural Networks
Speckle noise reduces the quality and nature of SAR imageries and diminishes the performance of SAR image processing. Thus, the multiplicative noise must be stifled before processing the image utilizing different image handling systems. Even though, there are number of speckle noise reduction techniques are available, all have its own merits and demerits. Therefore, noise reduction is still a major impediment in SAR image processing. In this paper, the speckle noise is reduced by using neural Network followed by the Bilateral Filter. This paper also presents the comparative analysis of two layered FFBPNN, TLFFBPNN and FLFFBPNN for speckle noise reduction of SAR images. Upon comparisons, it could be concluded that, TLFFBPNN de-speckling method provides good visual effects of SN reduction with better similarity and edging conservation metrics.
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