超低照度下微光图像的深度卷积自编码网络复原

刘超

doi:10.3788/OPE.20182604.0951

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超低照度下微光图像的深度卷积自编码网络复原

信息科学 | 更新时间：2020-07-05

- 超低照度下微光图像的深度卷积自编码网络复原
- Deep convolutional autoencoder networks approach to low-light level image restoration under extreme low-light illumination
- 光学精密工程 2018年26卷第4期页码：951-961
- 作者机构：
  
  海军工程大学兵器工程系, 湖北武汉 430033
- 作者简介：
  
  [ "刘超(1989-), 男, 陕西周至人, 博士研究生, 2012年于西安理工大学获得学士学位, 2014年于海军工程大学获得硕士学位, 主要从事机器视觉及彩色夜视方面的研究。E-mail:generaladolph@163.com" ]
- 基金信息：
  
  军内重点科研项目(427210843)
- DOI：10.3788/OPE.20182604.0951
  中图分类号： TN223
- 收稿日期：2017-08-18，
  
  录用日期：2017-9-26，
  
  纸质出版日期：2018-04-25
- 稿件说明：
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刘超. 超低照度下微光图像的深度卷积自编码网络复原[J]. 光学精密工程, 2018,26(4):951-961.

Chao LIU. Deep convolutional autoencoder networks approach to low-light level image restoration under extreme low-light illumination[J]. Optics and precision engineering, 2018, 26(4): 951-961.
刘超. 超低照度下微光图像的深度卷积自编码网络复原[J]. 光学精密工程, 2018,26(4):951-961. DOI： 10.3788/OPE.20182604.0951.

Chao LIU. Deep convolutional autoencoder networks approach to low-light level image restoration under extreme low-light illumination[J]. Optics and precision engineering, 2018, 26(4): 951-961. DOI： 10.3788/OPE.20182604.0951.

摘要

微光/红外图像彩色融合是目前国内外夜视技术的重要发展方向，在超低照度下（环境照度小于2×10

-3

lux），由于成像器件限制，微光图像具有低信噪比、低对比度等特点，导致目标难以辨识，成为制约彩色夜视技术的关键。为了提高目标的探测和识别率，提出了一种基于卷积自编码网络的微光图像复原方法，利用卷积自编码网络从微光图像训练集中学习超低照度下微光图像特征，实现去噪和对比度增强。实验结果表明，本文提出的方法得到的峰值信噪比（Peak Signal to Noise Ratio，PSNR）较经典的BM3D算法平均提高1.67 dB，结构相似度（Structural Similarity Index，SSIM）的值平均提高0.063，均方根对比度的值（Root Mean Square Contrast，RMSC）平均提高0.19。对微光图像复原具有很好的效果，能够有效地提高信噪比和对比度水平。

Abstract

LLL (Low-Light Level)/infrared image color fusion is an important development direction of night vision technology in the world. Under extreme low light level (environment illumination less than)

LLL image has low signal to noise ratio and low contrast features

and the target is difficult to identify as imaging device limitations

which has been a key constraint to color night vision technology. In order to improve target detection and recognition rate

LLL image restoration method based on the deep convolutional autoencoder network was proposed

which learning LLL image characteristics from the LLL image training set by using the convolutional autoencoder

and implementing de-noising and contrast enhancement. The experiment results show that

compared with the classical BM3D algorithm

the proposed method improves the peak signal to noise ratio (PSNR) with value of 1.67 dB and reduces the RMSE with value of 0.098

improves the structural similarity (SSIM) with the value of 0.063 and improves the root mean square contrast (RMSC) with value of 0.91. It has a very good effect on the restoration of low light level images

and improves the signal-to-noise ratio and contrast level effectively.

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Keywords

references

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