[1]林椹尠,张梦凯,吴成茂.利用残差密集网络的运动模糊复原方法[J].智能系统学报,2021,16(3):442-448.[doi:10.11992/tis.201912002]
LIN Zhenxian,ZHANG Mengkai,WU Chengmao.Image restoration with residual dense network[J].CAAI Transactions on Intelligent Systems,2021,16(3):442-448.[doi:10.11992/tis.201912002]
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LIN Zhenxian,ZHANG Mengkai,WU Chengmao.Image restoration with residual dense network[J].CAAI Transactions on Intelligent Systems,2021,16(3):442-448.[doi:10.11992/tis.201912002]
利用残差密集网络的运动模糊复原方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第3期
页码:
442-448
栏目:
学术论文—机器感知与模式识别
出版日期:
2021-05-05
- Title:
- Image restoration with residual dense network
- Keywords:
- image processing; motion pictures; image denoising; image restoration; deep learning; network model; network architecture; convolutional neural network
- 分类号:
- TP183
- 摘要:
- 针对图像生成过程中由于物体运动或相机抖动产生的运动模糊问题，提出了利用残差密集网络的运动模糊图像复原方法。设计对抗网络结构，以残差密集网络为生成器，通过长短连接实现不同层次特征的融合，生成复原图像，以深度卷积网络为判别器，判断图像真伪，在生成器和判别器的对抗中提高网络性能；采用对抗损失和内容损失结合的损失函数，提高网络的复原效果；以端到端的方式，省略模糊核的估计过程，输入模糊图像直接获取复原图像。实验结果表明，该方法能够取得较好的复原效果。
- Abstract:
- Aiming at the problem of motion blur caused by object motion or camera shake in the image generation process, an image restoration method based on the residual dense network is proposed. An adversarial network structure is used in the method, taking the residual dense network as the generator and fusing different levels of features in a long-short connection to generate restored images. The deep convolution network acts as the discriminator to identify the authenticities of images, training the network performance in adversarial between the generator and discriminator. The proposed method combines the adversarial and content losses to improve the effectiveness of network restoration. This study demonstrates the restoration of an input blur image in an end-to-end way without estimating the blur kernel. Experiments show that the proposed method results in a better restoration effect.
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备注/Memo
收稿日期:2019-12-02。
基金项目:国家自然科学基金项目(61671377)
作者简介:林椹尠，教授，博士，主要研究方向为基于小波理论的图像处理。发表学术论文30余篇;张梦凯，硕士研究生，主要研究方向为图像处理;吴成茂，高级工程师，主要研究方向为模式分析与智能信息处理、图像处理与信息安全。发表学术论文200余篇
通讯作者:张梦凯.E-maili:zmkdyx@163.com
更新日期/Last Update:
2021-06-25