ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2014, Vol. 51 ›› Issue (9): 1891-1900.doi: 10.7544/issn1000-1239.2014.20140266

所属专题: 2014深度学习

• 人工智能 •    下一篇

基于卷积神经网络的正则化方法

吕国豪, 罗四维, 黄雅平, 蒋欣兰   

  1. (北京交通大学交通数据分析与挖掘北京市重点实验室 北京 100044) (lvguohao@bjtu.edu.cn)
  • 出版日期: 2014-09-01

A Novel Regularization Method Based on Convolution Neural Network

Lü Guohao, Luo Siwei, Huang Yaping, Jiang Xinlan   

  1. (Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044)
  • Online: 2014-09-01

摘要: 正则化方法是逆问题求解中经常使用的方法.准确的正则化模型在逆问题求解中具有重要作用.对于不同类型的图像和图像的不同区域,正则化方法的能量约束形式应当不同,但传统的L1,L2正则化方法均基于单一先验假设,对所有图像使用同一能量约束形式.针对传统正则化模型中单一先验假设的缺陷,提出了基于卷积神经网络的正则化方法,并将其应用于图像复原问题.该方法的创新之处在于将图像复原看作一个分类问题,利用卷积神经网络对图像子块的特征进行提取和分类,然后针对不同特征区域采用不同的先验形式进行正则化约束,使正则化方法不再局限于单一的先验假设.实验表明基于卷积神经网络的正则化方法的图像复原结果优于传统的单一先验假设模型.

关键词: L1范数约束, L2范数约束, 正则化方法, 卷积神经网络, 图像复原

Abstract: Regularization method is widely used in solving the inverse problem. An accurate regularization model plays the most important part in solving the inverse problem. The energy constraints should be different for the different types of images and different parts of the same image, but the traditional L1 and L2 models used in the field of image restoration are both based on a single prior assumption. In this paper, according to the defects of the single priori assumption in traditional regularization model, a novel regularization method based on convolution neural network is proposed and applied to image restoration, therefore, the image restoration can be regarded as a classification issue. In this method, the image is partitioned into several blocks, and the convolution neural network is used to extract and classify the features of sub-block images; then the different forms of the priori regularization constraints are adopted considering the different features of the sub-block images, therefore the regularization method is no longer limited to a single priori assumption. Experiments show that the image restoration results by the regularization method based on convolution neural network are superior to those by the traditional regularization model with a single priori assumption. Therefore the regularization method based on convolution neural network can better restore image, maintain the edge texture characteristic of the image nicely, and has lower computational cost.

Key words: L1 norm constraint, L2 norm constraint, regularization method, convolution neural network, image restoration

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