Deep convolutional autoencoder networks approach to low-light level image restoration under extreme low-light illumination

Chao LIU

doi:10.3788/OPE.20182604.0951

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Deep convolutional autoencoder networks approach to low-light level image restoration under extreme low-light illumination

Information Sciences | 更新时间：2020-07-05

- Deep convolutional autoencoder networks approach to low-light level image restoration under extreme low-light illumination
- Optics and Precision Engineering Vol. 26, Issue 4, Pages: 951-961(2018)
- 作者机构：
  
  海军工程大学兵器工程系, 湖北武汉 430033
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182604.0951
  CLC： TN223
- Received：18 August 2017，
  
  Accepted：26 September 2017，
  
  Published：25 April 2018
- 稿件说明：
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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：

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.

关键词

Keywords

references

曾桂林, 苏秉华, 牛丽红, 等.基于神经网络的微光图像增强与复原[J].兵工学报, 2006, 27(4):652-654.

ZENG G L, SU B H, NIU L H, et al.. Neural network-based low-light-level image enhancement and reconstruction[J]. Acta Armamentarii, 2006, 27(4):652-654. (in Chinese)

金伟其, 陶禹, 石峰, 等.微光视频器件及其技术的进展[J].红外与激光工程, 2015, 44(11):3167-3176.

JIN W Q, TAO Y, SHI F, et al.. Progress of low level light video technology[J]. Infrared and Laser Engineering, 2015, 44(11):3167-3176. (in Chinese)

鲁佳颖, 谷小婧, 顾幸生.面向微光/红外融合彩色夜视的场景解析方法[J].红外与激光工程, 2017, 46(8):0804002.

LU J Y, GU X J, GU X SH. Scene parsing method toward low-light-level/infrared color night vision[J]. Infrared and Laser Engineering, 2017, 46(8):0804002. (in Chinese)

ROUDOT P, KERVRANN C, BOULANGER J, et al. . Noise modeling for intensified camera in fluorescence imaging: Application to image denoising[C]. Proceedings of 2013 IEEE 10 th International Symposium on Biomedical Imaging ( ISBI ), IEEE , 2013: 600-603.

QUAN J. Image denoising of Gaussian and Poisson noise based on wavelet thresholding [D]. Cincinnati: University of Cincinnati, 2013.

VINCENT P, LAROCHELLE H, BENGIO Y, et al. . Extracting and composing robust features with denoising autoencoders[C]. Proceedings of the 25 th International Conference on Machine Learning , ACM , 2008: 1096-1103.

XIE J Y, XU L L, CHEN E H. Image denoising and inpainting with deep neural networks[C]. Proceedings of the 25 th International Conference on Neural Information Processing Systems , ACM , 2012: 341-349.

BURGER H C, SCHULER C J, HARMELING S. Image denoising: Can plain neural networks compete with BM3D?[C]. Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ), IEEE , 2012: 2392-2399.

AGOSTINELLI F, ANDERSON M R, LEE H. Adaptive multi-column deep neural networks with application to robust image denoising[C]. Advances in Neural Information Processing Systems , NIPS , 2013: 1493-1501.

WU Y W, ZHAO H H, ZHANG L Q. Image denoising with rectified linear units[C]. Proceedings of the 21 st International Conference on Neural Information Processing , Springer , 2014: 142-149.

LORE K G, AKINTAYO A, SARKAR S. LLNet:A deep autoencoder approach to natural low-light image enhancement[J]. Pattern Recognition, 2017, 61:650-662.

DONG CH, DENG Y B, LOY C C, et al. . Compression artifacts reduction by a deep convolutional network[C]. Proceedings of 2015 IEEE International Conference on Computer Vision ( ICCV ), IEEE , 2015: 576-584.

MAO X J, SHEN CH H, YANG Y B. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections[C]. Advances in Neural Information Processing Systems , NIPS , 2016.

肖龙, 徐超, 刘广荣.应用于可穿戴微光成像系统的嵌入式平台设计[J].红外与激光工程, 2016, 45(1):0118006.

XIAO L, XU CH, LIU G R. Design of embedded platform applied in wearable low-light level imaging system[J]. Infrared and Laser Engineering, 2016, 45(1):0118006. (in Chinese)

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.

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Related Institution

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