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Restoration and segmentation of digital images by adaptive filtering.

机译：通过自适应滤波对数字图像进行恢复和分割。

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Segmentation of degraded images has always been a difficult problem to solve. In coherent image acquisition systems, occurrence of speckle noise is a common phenomenon that is hard to remove without further degrading the image. In this dissertation, a new method for image segmentation based on the adaptive fuzzy leader clustering algorithm (AFLC) is introduced. AFLC is a hybrid neuro-fuzzy model developed by integrating a learning vector quantization (LVQ) network with fuzzy memberships. This integration provides a powerful yet fast method for recognizing embedded data structure and it has shown superior misclassification rates over similar segmentation approaches. Neuro-fuzzy clustering algorithms can achieve efficient object extraction from noisy images since noise pixels can be identified during the clustering process and separated from the rest of the image. When dealing with corrupted images, the first step prior to segmentation is always the enhancement of features in the image by filtering in either the spatial or frequency domain. In this dissertation, a new non-linear adaptive filter based on AFLC is developed. This new adaptive filtering method has been specifically tailored to reduce the degradation introduced by speckle noise in coherent imagery like synthetic aperture radar (SAR) or ultrasound imaging. The results achieved by this process have been compared with the results from the traditional median filter, the Kuan filter, and the connectivity-preserving morphological filter demonstrating the superior performance of AFLC in removing speckle noise. We have also compared AFLC to other classification algorithms, such as those derived from the statistical decision theory, and to many well-known fuzzy, neural, and neuro-fuzzy unsupervised algorithms. The concept of local cluster validity introduced in AFLC is addressed and comparison results are presented for well-known global validity indices. Finally, the convergence criteria of the AFLC algorithm have been analyzed under the framework of stochastic approximation and results that ensure the stability of its performance are presented.
机译：降级图像的分割一直是难以解决的问题。在相干图像采集系统中，斑点噪声的出现是一种常见现象，如果不进一步降低图像质量就很难消除。本文提出了一种基于自适应模糊领导聚类算法的图像分割新方法。 AFLC是通过将学习向量量化（LVQ）网络与模糊隶属度集成而开发的混合神经模糊模型。这种集成提供了一种功能强大而又快速的方法来识别嵌入式数据结构，并且与类似的分割方法相比，它具有更高的误分类率。神经模糊聚类算法可以从嘈杂的图像中实现有效的对象提取，因为可以在聚类过程中识别出噪声像素并将其与图像的其余部分分开。当处理损坏的图像时，分割之前的第一步始终是通过在空间或频域中进行滤波来增强图像中的特征。本文研究了一种基于AFLC的非线性自适应滤波器。这种新的自适应滤波方法经过专门调整，可以减少在相干图像（如合成孔径雷达（SAR）或超声成像）中由斑点噪声引起的降级。通过此过程获得的结果已与传统中值滤波器，Kuan滤波器和保留连接性的形态滤波器的结果进行了比较，证明了AFLC在去除斑点噪声方面的卓越性能。我们还将AFLC与其他分类算法（例如从统计决策理论派生的算法）以及许多知名的模糊，神经和神经模糊无监督算法进行了比较。提出了在AFLC中引入的局部聚类有效性的概念，并给出了众所周知的全局有效性指标的比较结果。最后，在随机逼近的框架下分析了AFLC算法的收敛准则，并给出了确保其性能稳定的结果。

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