一种深度残差学习的含噪图像轮廓重建方法  

Contour reconstruction method for noisy image based on depth residual learning

作　　者：王晓明[1,2] 张淑艳 张婕[1] 原思聪[2] WANG Xiaoming;ZHANG Shuyan;ZHANG Jie;YUAN Sicong(School of Science,Xi’an University of Architechure and Technology,Xi’an 710055,China;School of Mechanical and Electrical Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China)

机构地区：[1]西安建筑科技大学理学院,陕西西安710055 [2]西安建筑科技大学机电工程学院,陕西西安710055

出　　处：《西安电子科技大学学报》2020年第3期66-71,共6页Journal of Xidian University

基　　金：国家自然科学基金(51878546);陕西省教育厅科技计划(17JK0453);陕西省自然科学基金(2018JM5090)。

摘　　要：为了提高识别含噪图像的能力,提出一种基于深度残差学习的含噪图像轮廓重建方法。采用锐化模板匹配技术进行含噪图像信息的增强处理;利用图像的局部灰度信息构建图像的边缘活动轮廓模型;采用活动轮廓套索方法进行图像高分辨重构;提取含噪图像的局部灰度能量项与局部梯度能量项特征量,构建卷积神经网络分类器进行特征分类;结合图像灰度直方图的相似性判断学习的卷积神经网络的学习深度,提升图像细节信息的分辨能力,实现含噪图像的轮廓高分辨重建。仿真结果表明,采用该方法进行含噪模糊图像重建的分辨能力较高,输出峰值信噪比较高,有效地提升了图像的识别能力。In order to improve the recognition ability of noisy images,a method of contour reconstruction based on depth residuals learning is proposed.The sharpening template matching technique is used to enhance the noisy image information,the local gray level information on the image is used to construct the edge active contour model of the image,and the active contour lasso method is used to reconstruct the image with a high resolution.The feature quantities of local gray energy and local gradient energy of the noisy image are extracted,and a convolutional neural network classifier is constructed to classify the features.The learning depth of the learning convolutional neural network is judged by combining the similarity of the gray histogram of the image.The resolution ability of image detail information is improved,and the contour high resolution reconstruction of the noisy image is realized.Simulation results show that the proposed method has a high resolution and a high peak signal to noise ratio(PSNR),which improves the recognition ability of the image effectively.

关 键 词：图像识别 深度学习 卷积神经网络 轮廓重建 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]