Civicioglu Pinar
Department of Aircraft Electrics and Electronics, Civil Aviation School, Erciyes University, Kayseri, Turkey.
IEEE Trans Image Process. 2007 Mar;16(3):759-73. doi: 10.1109/tip.2007.891067.
In this paper, a novel adaptive network-based fuzzy inference system (ANFIS)-based filter, ABF, is presented for the restoration of images corrupted by impulsive noise (IN). The ABF is performed in two steps. In the first step, impulse detection is realized by using statistical tools. In the second step, a nonlinear filtering scheme based on ANFIS is performed for only the corrupted pixels detected in the first step. To demonstrate the effectivity of ABF at the removal of high-level IN, extensive simulations were realized for ABF and nine different comparison filters. Empirical results indicate that the proposed filter achieves a better performance than the comparison filters in terms of noise suppression and detail preservation, even when the images are highly corrupted by IN.
本文提出了一种基于新型自适应网络模糊推理系统（ANFIS）的滤波器ABF，用于恢复受脉冲噪声（IN）污染的图像。ABF分两步执行。第一步，利用统计工具实现脉冲检测。第二步，仅对第一步中检测到的受损像素执行基于ANFIS的非线性滤波方案。为证明ABF在去除高水平IN方面的有效性，对ABF和九种不同的比较滤波器进行了广泛的仿真。实验结果表明，即使图像受到严重的IN污染，所提出的滤波器在噪声抑制和细节保留方面也比比较滤波器具有更好的性能。
