构建带空洞卷积的深度神经网络重建高分辨率图像

doi:10.16451/j.cnki.issn1003-6059.201903007

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摘要为了在滤波器参数保持不变的情形下扩大感受野,在非常深的卷积网络超分辨率模型网络中引入空洞卷积方法.首先,分析不同膨胀系数组合的空洞卷积块的感受野,并选择更好的结构作为空洞卷积块.然后,堆叠卷积块并加入残差连接构成深度卷积网络.最后,使用多种训练技巧对网络进行重新训练.实验表明,对于数据集Set5上较大的扩大因子,文中构建网络可提升重建效果,并在视觉上也有明显优势.

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	张焯林
	赵建伟
	曹飞龙

关键词 ：超分辨率重建, 卷积神经网络, 空洞卷积, 残差连接

Abstract：To expand the perception field with the filter parameters unchanged, dilated convolution is introduced into very deep convolutional networks super-resolution model. Firstly, the perception field of the dilated convolution block with different expansion coefficients is analyzed and a better combination structure is selected as the dilated convolution block. Then, the deep convolution network is constructed by stacking convolution blocks and adding residual connection. Experiment shows that the reconstruction effect can be improved by the constructed network for the larger scaling factors of Set5 dataset. Besides, there are obvious visual advantages.

Key words： Super Resolution Reconstruction Convolutional Neural Network Dilated Convolution Residual Connection

收稿日期: 2018-11-12

ZTFLH:	TN 911.71
	TP 183

基金资助:国家自然科学基金项目(No.61672477)、浙江省自然科学基金项目(No.LY18F020018)资助

通讯作者: 曹飞龙, 博士,教授,主要研究方向为深度学习、图像处理等.E-mail:feilongcao@gmail.com.

作者简介: 张焯林,硕士研究生,主要研究方向为深度学习、图像处理等.E-mail:847245720@qq.com.
赵建伟,博士,教授,主要研究方向为深度学习、图像处理等.E-mail:zhaojw@amss.ac.cn.

引用本文:

张焯林,赵建伟,曹飞龙. 构建带空洞卷积的深度神经网络重建高分辨率图像[J]. 模式识别与人工智能, 2019, 32(3): 259-267. ZHANG Zhuolin, ZHAO Jianwei, CAO Feilong. Building Deep Neural Networks with Dilated Convolutions to Reconstruct High-Resolution Image. , 2019, 32(3): 259-267.

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