一种改进的噪声图像语义分割方法

在图像的获取和传输过程中,可能会出现噪声, 它不仅破坏了图像的真实信息,而且严重影响了图像的视觉效果。因此, 噪声图像的语义分割成为图像分析中最具挑战性的问题之一。为了提高噪声图像的分割性能 ,本文在分析全卷积网络(FCN)的 基础上,提出一种改进的FCN模型(IFCN)对噪声图像语义分割。该算法采用一种新的中值 池化方法代替卷积神经网络的最大值 池化,可以在去除噪声的同时保留更多边缘信息。在训练整个深度网络时,通过反向传播算 法以一种直接的端到端,像素到像素 的方式映射。实验结果表明,提出的模型在PASCAL VOC2012数据集上对噪声图像语义分割 可以获得比较好的分割效果,准确率mean IU达到86.5%。

An improved semantic segmentation method for noisy image

Noise may be arisen in the capturing and transmission process of the i mage,which not only corrupts the true information of an image,but also seriously affects the visual effects of the image.Therefore,th e semantic segmentation of noisy images becomes one of the most challenging problems in image analysis.In order to improve the segmentation performance of noisy image,based on the analyses of fully convolutional network (FCN) and the deep convolutional neural network (DCNN),we propose an improved F CN model (IFCN) for semantic segmentation of noisy images.The algorithm introduces a new median pooling method instead of the commonly used max pooling method in convolutional neural network (CNN),which can remove noise and preserve more boundaries information.Our model trains the whole deep network by a direct end-to-end,pixels-to-pi xels mapping way with the back propagation algorithm.By fine-tuning the network structure and adjusting the different parameters,respe ctively,we train and test images with salt and pepper noise and gaussian noise.Experimental results show that the proposed model can achieve a better performance for semantic segmentation of noisy images on Pascal VOC2012dataset,and the accuracy of mean IU is 86.5%, which indicates the effectiveness of the method.