Denoising Method for Deep Shaft Lining Images Based on Convolution Neural Network

doi:10.7652/xjtuxb201906016

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Denoising Method for Deep Shaft Lining Images Based on Convolution Neural Network

更新时间：2025-07-09

- Denoising Method for Deep Shaft Lining Images Based on Convolution Neural Network
- Vol. 53, Issue 6, Pages: 117-124(2019)
- 作者机构：
  
  安徽理工大学省部共建深部煤矿采动响应与灾害防控国家重点实验室,安徽,淮南,232001
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201906016
  CLC： TP391
- Online First：10 June 2019，
  
  Published：2019
- 稿件说明：
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Denoising Method for Deep Shaft Lining Images Based on Convolution Neural Network[J]. 2019, 53(6): 117-124.
DOI：

Denoising Method for Deep Shaft Lining Images Based on Convolution Neural Network[J]. 2019, 53(6): 117-124. DOI： 10.7652/xjtuxb201906016.

摘要

为了实现井壁缺陷的自动检测

提出去除井壁图像噪声的卷积神经网络(CNN)模型(ELU-CNN)。该模型为深28层的全卷积网络模型

由5个特征提取模块(FEM)和跳跃连接组成; 跳跃连接将第一卷积层的输出特征与每一个FEM的输出特征串联融合

保证图像特征的充分提取; 使用残差学习来缓解梯度消失并提高收敛速度

保证训练后的去噪模型学习到的非线性映射是图像噪声; 选用ELU作为激活函数

它具有软饱和性且输出均值接近于零

能增强模型对输入噪声的鲁棒性并加速模型收敛。在标准测试集BSD68、set12及实际井壁图像上

验证ELU-CNN模型的去噪性能并和先进方法作比较

实验结果表明:与FFDNet模型相比

ELU-CNN模型的平均峰值信噪比

在含噪声浓度σ为(15

75)的BSD68、set12测试集上分别提高了(0.17

0.11

0.08

0.05

0.03)dB、(0.18

0.16

0.08

0.06

0.07)dB。在去除井壁图像盲噪声时

ELU-CNN模型能更好地保留缺陷的纹理信息。

Abstract

An ELU-CNN image denoising model based on convolution neural network(CNN)is proposed to realize automatic detection of the defects on the shaft lining. It is a 28-layer full-convolution denoising network model and consists of five feature extraction modules(FEMs)and skip connections. The skip connections combine the output features of the first convolution layer with the output features of each FEM to ensure the full extraction of image features. The residual learning is used in the ELU-CNN model to relieve the gradient disappearance problem

to improve the convergence speed

and to ensure that the learned nonlinear mapping from a noisy image by the denoising model after training is a noise image. ELU is used as an activation function. It has soft saturation performance

and the average of its outputs is close to zero. These properties can accelerate the convergence of the model and enhance the robustness of the model to the input noises. The denoising effect of ELU-CNN is verified on the standard test sets BSD68 and set12 as well as actual shaft wall images

and is compared with those of some advanced methods. The experimental results on BSD68 and set12 with noise concentration σ=(15

75)and comparisons with FFDNet model show that the average of peak signal to noise ratios of ELU-CNN increases(0.17

0.11

0.08

0.05

0.03)dB and(0.18

0.16

0.08

0.06

0.07)dB

respectively. ELU-CNN can better preserve the texture information of fractures when removing blind noise from shaft wall images.

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Keywords

references

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