有效滤除高强度图像混合噪声的方法

An Effective Method of High-Intensity Mixed Noise Filtering for Images

传统的交叉视觉皮质模型(ICM)对单一噪声的去除具有良好的性能.为了扩展ICM在图像降噪领域的应用,提高降噪能力,提出一种基于邻域连接的NL-ICM.针对传统ICM存在的局限性,在神经元的构造上引入双边滤波的思想,通过扩展神经元的连接输入、引入连接权重、设计脉冲阈值实时计算函数,并为神经元设计像素更新规则.实验结果表明,该模型能够较好地去除图像中的混合噪声.

Intersecting cortical model（ICM） is only capable of filtering images with only one single type of noise.In order to extend the application of ICM in image denoising,ICM neurons＇ connection is re-designed.In our work,the thought of Bilateral Filtering is introduced together with extending the connecting input of neurons.By considering the linking-weight,designing a real-time pulse threshold function and a pixel renewal rule,a new Neighborhood-Linking ICM is proposed in this paper.Experiments show that the proposed ICM-based filtering method is fast and effective for removing the impulse noises mixed with additional Gaussian noises.