用于脉冲噪声图像的交叉视觉皮质模型滤波

Intersecting Cortical Model Filter for Impulse Noise Image

脉冲噪声点与其局部周围所在图像像素灰度值之间有很大的差异，导致了交叉视觉皮质模型（ICM）中神经元激发顺序的不同，根据ICM所具有相似神经元同步激发从而产生脉冲输出的特性，对脉冲噪声点进行定位和自适应滤波．结果表明：文中方法处理的受脉冲噪声污染的图像的信噪比较其他非线性滤波器的处理结果提高50％～60％，运算耗时仅为其他非线性滤波器的20％～30％；同时，比其他非线性滤波方案更有效地保持了图像的高频细节信息，给图像的后期处理和识别打下了很好的基础．

The apparent difference of gray value between impulse noised pixels and the pixels around them causes the neurons of intersecting cortical model （ICM） fired at different stage. In this paper we used the pulse output generated from similar neurons synchronized fire mechanism in the ICM to locate the impulse noise inside the image, and adaptively filtered the impulse noised pixels. The signal to noise ratio （SNR） generated from the ICM filter proposed in this paper is increased by 50 % to 60 % than those from other nonliner filters, and the computation time is only about 20 % to 30% of them. In the meantime, the proposed algorithm can effectively preserve the details of image, which is greatly helpful for further image processing and recognition.