为了使椒盐噪声不影响图像的后续处理, 提出一种基于BP 神经网络噪声检测的自适应开关滤波器来检测和滤除图像椒盐噪声。该方法利用像素值及其邻域特性作为像素点的描述即神经网络的输入, 通过神经网络自动检测图像的噪声位置, 据此保持非噪声点不变, 对噪声点进行自适应窗口大小的均值滤波处理, 且仅窗口内非噪声点参与均值运算。实验结果表明, 本方法中BP 网络检测椒盐噪声效率高, 整个滤波过程无需针对不同图像设置参数, 滤波操作简单且性能优良, 在去噪效果、细节保持和减少时间耗费等方面有一定优势。

To remove impulse noise from image before some other process, an adaptive switching filter based-on neural network noise detector is proposed. This method describes each pixel with pixel value and its neighborhood characteristics and takes these as inputs of the neural network to identify pixels which are likely to be contaminated by noise with the trained neural network automatically. According to the idea of switching filter, the noisy pixels detected are processed by mean filter with adaptive window size, and only noise-free pixels of the window are involved in the average computation. Compared with some other common filters, the experimental result shows that this BP neural network has high accuracy of salt and pepper noise detection. Besides, this filtering process is superior in denoising effect, details preserving and time consuming reduction without manual intervention.