基于卷积神经网络的遥感图像去噪算法

魏江; 刘潇; 梅少辉

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基于卷积神经网络的遥感图像去噪算法

更新时间：2026-09-04

- 基于卷积神经网络的遥感图像去噪算法
- Remote sensing image denoising based on convolutional neural network
- 微电子学与计算机 2019年36卷第8期页码：59-62
- 作者机构：
  
  西北工业大学电子信息学院,陕西,西安,710129
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目 61671383
- DOI：
  中图分类号：
- 纸质出版：2019
- 稿件说明：
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魏江, 刘潇, 梅少辉. 基于卷积神经网络的遥感图像去噪算法[J]. 微电子学与计算机, 2019, 36(8): 59-62, 67.

WEI Jiang, LIU Xiao, MEI Shao-hui. Remote sensing image denoising based on convolutional neural network[J]. 2019, 36(8): 59-62.
魏江, 刘潇, 梅少辉. 基于卷积神经网络的遥感图像去噪算法[J]. 微电子学与计算机, 2019, 36(8): 59-62, 67. DOI：

WEI Jiang, LIU Xiao, MEI Shao-hui. Remote sensing image denoising based on convolutional neural network[J]. 2019, 36(8): 59-62. DOI：

摘要

由于成像设备的局限和外部环境的干扰等因素影响

遥感图像在信息数字化和传输过程中常常包含大量噪声

导致图像质量下降

对后续图像处理产生不利影响.本文提出了一种基于卷积神经网络的遥感图像去噪算法

将图像去噪过程作为神经网络的拟合过程

使用含有批量正则化层的深度神经网络

利用残差学习策略

可以对多种噪声等级的遥感图像进行去噪处理.实验表明

本文算法不仅有效提升了去噪效果

还缩短了网络的训练时间.

Abstract

In the process of digitizing and transmitting information

due to the limitations of imaging equipment and the interference of external environment

remote sensing images often contain a lot of noise

which leads to the degradation of image quality and adversely affects subsequent image processing. In this paper

a remote sensing image denoising algorithm based on convolutional neural network is proposed. The algorithm considers the image denoising process as the neural network fitting process. Using the residual learning strategy and the deep neural network with batch regularization layer

it can denoise the remote sensing images with various noise levels. Experiments show that the proposed algorithm not only effectively improves the denoising effect

but also shortens the training time of the network.

关键词

Keywords

references

PORTILLA J, STRELA V, WAINWRIGHT M J, et al. Image denoising using scale mixtures of Gaussians in the wavelet domain [J]. IEEE Transactions on Image Processing, 2003, 12(11): 1338- 1351. DOI: 1338

DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering [J]. IEEE Transactions on Image Processing, 2007, 16(8):2080-2095. DOI: 2080

IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift [C]. 32nd International Conference on Machine Learning. Lile: International Machine Learning Society, 2015: 448-456.

HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition [C]. Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE Computer Society, 2016: 770-778.

KINGMA D P, BA J. Adam: A Method for Stochastic Optimization [C]. 3rd International Conference on Learning Representations. San Diego, 2015: arXiv: 1412.6980.

ZHANG K, ZUO W, CHEN Y, et al. Beyond a Gaussian denoiser: Residual learning of Deep CNN for image denoising [J]. IEEE Transactions on Image Processing, 2017, 26(7): 3142- 3155. DOI: 3142

AHARON M, ELAD M, BRUCKSTEIN A. The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal processing, 2006, 54(11); 4311-4322. DOI: 4311

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