摘要
在日常生活中,由于系统和设备问题,图片经常会引入噪声,图像也会因此变得很模糊.而分水岭算法经常因为噪声的缘故使图像出现过分割.针对传统分水岭算法进行图像分割易受噪声影响的问题,研究了一种基于DnCNN函数的分水岭算法.基于当下神经网络对于图像识别、归类、特征提取这一领域所做出的卓越成绩,利用神经网络的良好图像统计特性进行图像去噪,并改进传统的激活函数,图像经过神经网络去噪后图像细节更加明显和清晰,再利用改进的分水岭算法,使用最大相似性合并规则对去噪后的图像进行分割.并将该算法与K-means算法和噪声图片+分水岭算法这两个算法进行比较,基于DnCNN函数的分水岭算法对图像的分割精度更高.
In daily life,noise,which is often introduced into pictures because of system and equipment problems,blurs images dramatically.However,watershed algorithm often causes image segmentation because of noise.In order to solve the problem that the traditional watershed algorithm in image segmenting is easily affected by noise,the current study was conducted on a watershed algorithm based on DnCNN function.With reference to the outstanding achievements made by the current neural network in the field of image recognition,classification and feature extraction,the image denoising was carried out by adopting the good image statistical characteristics of the neural network,and thus the traditional activation function was improved.After the image was denoised by the neural network,the details of the image are more obvious and clearer.Furthermore,by applying the improved watershed algorithm and the maximum similarity merging rule,the denoised image was segmented.The comparison between the three algorithms -the current algorithm,the K-means algorithm and the noise picture+watershed algorithm-showed that the segmentation accuracy of the image segmented by the watershed algorithm based on DnCNN function is the highest.
作者
邓祾
DENG Ling(School of Electrical Engineering,Anhui University of Engineering,Wuhu Anhui 241000,China)
出处
《海南热带海洋学院学报》
2019年第5期69-75,共7页
Journal of Hainan Tropical Ocean University
关键词
神经网络
图像识别
分水岭算法
neural network
image recognition
watershed algorithm
