论文标题
非极值价值的两阶段方法去除盐和胡椒噪声
A Two-stage Method for Non-extreme Value Salt-and-Pepper Noise Removal
论文作者
论文摘要
以前有几种基于神经网络的方法可以在降低盐和胡椒噪声方面具有出色的性能。但是，这些方法是基于一个假设，即盐和胡椒噪声的价值正好是0和255。在现实世界中不是正确的。当值与0和255不同时，这些方法的结果会急剧偏离。为了克服这种弱点，我们的方法旨在设计卷积神经网络以检测噪声像素在更大的值中的噪声像素，然后使用过滤器将像素值修改为0，这对进一步的过滤是有益的。此外，另一个卷积神经网络用于进行转化和恢复工作。
There are several previous methods based on neural network can have great performance in denoising salt and pepper noise. However, those methods are based on a hypothesis that the value of salt and pepper noise is exactly 0 and 255. It is not true in the real world. The result of those methods deviate sharply when the value is different from 0 and 255. To overcome this weakness, our method aims at designing a convolutional neural network to detect the noise pixels in a wider range of value and then a filter is used to modify pixel value to 0, which is beneficial for further filtering. Additionally, another convolutional neural network is used to conduct the denoising and restoration work.
