LLNet: A deep autoencoder approach to natural low-light image enhancement

In surveillance, monitoring and tactical reconnaissance, gathering visual information from a dynamic environment and accurately processing such data are essential to making informed decisions and ensuring the success of a mission. Camera sensors are often cost-limited to capture clear images or vide...

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Veröffentlicht in:	Pattern recognition Jg. 61; S. 650 - 662
Hauptverfasser:	Lore, Kin Gwn, Akintayo, Adedotun, Sarkar, Soumik
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.01.2017
Schlagworte:	Deep autoencoders Image enhancement Natural low-light images Image enhancement Deep autoencoders Natural low-light images
ISSN:	0031-3203, 1873-5142
Online-Zugang:	Volltext
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Abstract	In surveillance, monitoring and tactical reconnaissance, gathering visual information from a dynamic environment and accurately processing such data are essential to making informed decisions and ensuring the success of a mission. Camera sensors are often cost-limited to capture clear images or videos taken in a poorly-lit environment. Many applications aim to enhance brightness, contrast and reduce noise content from the images in an on-board real-time manner. We propose a deep autoencoder-based approach to identify signal features from low-light images and adaptively brighten images without over-amplifying/saturating the lighter parts in images with a high dynamic range. We show that a variant of the stacked-sparse denoising autoencoder can learn from synthetically darkened and noise-added training examples to adaptively enhance images taken from natural low-light environment and/or are hardware-degraded. Results show significant credibility of the approach both visually and by quantitative comparison with various techniques. •Novel application of stacked sparse denoising autoencoder enhances low-light images.•Simultaneous learning of contrast-enhancement and denoising (LLNet).•Sequential learning of contrast-enhancement and denoising (Staged LLNet).•Synthetically trained model evaluated on natural low-light images.•Learned features visualized to gain insights about the model.
AbstractList	In surveillance, monitoring and tactical reconnaissance, gathering visual information from a dynamic environment and accurately processing such data are essential to making informed decisions and ensuring the success of a mission. Camera sensors are often cost-limited to capture clear images or videos taken in a poorly-lit environment. Many applications aim to enhance brightness, contrast and reduce noise content from the images in an on-board real-time manner. We propose a deep autoencoder-based approach to identify signal features from low-light images and adaptively brighten images without over-amplifying/saturating the lighter parts in images with a high dynamic range. We show that a variant of the stacked-sparse denoising autoencoder can learn from synthetically darkened and noise-added training examples to adaptively enhance images taken from natural low-light environment and/or are hardware-degraded. Results show significant credibility of the approach both visually and by quantitative comparison with various techniques. •Novel application of stacked sparse denoising autoencoder enhances low-light images.•Simultaneous learning of contrast-enhancement and denoising (LLNet).•Sequential learning of contrast-enhancement and denoising (Staged LLNet).•Synthetically trained model evaluated on natural low-light images.•Learned features visualized to gain insights about the model.
Author	Lore, Kin Gwn Akintayo, Adedotun Sarkar, Soumik
Author_xml	– sequence: 1 givenname: Kin Gwn surname: Lore fullname: Lore, Kin Gwn email: kglore@iastate.edu – sequence: 2 givenname: Adedotun surname: Akintayo fullname: Akintayo, Adedotun email: akintayo@iastate.edu – sequence: 3 givenname: Soumik surname: Sarkar fullname: Sarkar, Soumik email: soumiks@iastate.edu
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Cites_doi	10.1007/978-3-319-11758-4_10 10.1109/CVPR.2012.6247952 10.1109/ICCPS.2016.7479095 10.1109/TIP.2006.881969 10.1016/j.dsp.2013.06.002 10.1016/j.dsp.2003.07.002 10.1016/S0734-189X(87)80186-X 10.1109/TIP.2010.2092438 10.36001/phmconf.2015.v7i1.2723 10.1109/ICPR.1992.202045 10.1109/TIP.2003.819861 10.1109/TIP.2007.901238 10.1109/ICME.2011.6012107 10.1109/CVPR.2004.1315150 10.1109/TIP.2005.852196 10.1145/1390156.1390294 10.25080/Majora-92bf1922-003 10.1007/s11265-016-1209-3 10.1109/ICPR.2008.4761424 10.1007/BF03178082
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References	A. Yamasaki, H. Takauji, S. Kaneko, T. Kanade, H. Ohki, Denighting: Enhancement of nighttime images for a surveillance camera, in: 19th International Conference on Pattern Recognition, 2008. ICPR 2008, IEEE, Tampa, FL, 2008, pp. 1–4. Dabov, Foi, Katkovnik, Egiazarian (bib15) 2007; 16 Chan, Ho, Nikolova (bib17) 2005; 14 J. Xie, L. Xu, E. Chen, Image denoising and inpainting with deep neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 341–349. Pisano, Zong, Hemminger, DeLuce, Johnston, Muller, Braeuning, Pizer (bib10) 1998; 11 S. Sarkar, K.G. Lore, S. Sarkar, V. Ramanan, S.R. Chakravarthy, S. Phoha, A. Ray, Early detection of combustion instability from hi-speed flame images via deep learning and symbolic time series analysis, Proceedings of the Annual Conference of the Prognostics and Health Management Society, Coronado, California, 2015 F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron, N. Bouchard, Y. Bengio, Theano: new features and speed improvements, in: Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012. Bergstra, Bengio (bib32) 2012; 13 K.G. Lore, N. Sweet, K. Kumar, N. Ahmed, S. Sarkar, Deep value of information estimators for collaborative human–machine information gathering, in: International Conference of Cyber-physical Systems (ICCPS), Vienna, Austria, 2016. Santoso, Nugroho, Suparta, Hidayat (bib26) 2011; 2 Kaur, Kaur, Kaur (bib12) 2011; 2 A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada, 2012. Paper PDF is found here Y. LeCun, F.J. Huang, L. Bottou, Learning methods for generic object recognition with invariance to pose and lighting, in: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, vol. 2, IEEE, Washington, DC.,2004, pp. II–97. S. Sarkar, V. Venugopalan, K. Reddy, J. Ryde, M. Giering, N. Jaitly, Occlusion edge detection in rgbd frames using deep convolutional neural networks, in: Proceedings of IEEE High Performance Extreme Computing Conference, Waltham, MA, 2015. P. Vincent, H. Larochelle, Y. Bengio, Extracting and composing robust features with denoising autoencoders, in: Proceedings of the 25th International conference on Machine Learning-ICML'08, 2008, pp. 1096–1103. C. Couprie, C. Farabet, L. Najman, Y. LeCun, Indoor semantic segmentation using depth information, in: ICLR, 2013. P. Trahanias, A. Venetsanopoulos, Color image enhancement through 3-d histogram equalization, in: Proceedings of 11th IAPR International Conference on Pattern Recognition, 1992. Vol. III. Conference C: Image, Speech and Signal Analysis, IEEE, The Hague, The Netherlands, 1992, pp. 545–548. X. Dong, G. Wang, Y.A. Pang, W. Li, J.G. Wen, W. Meng, Y. Lu, Fast efficient algorithm for enhancement of low lighting video, in: 2011 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Barcelona, 2011, pp. 1–6. Wu (bib13) 2011; 20 R. Gonzalex, R. Woods, Digital image Processing, 2nd Edition, no. 0-201-28075-8 in 0-201-28075-8, Prentice Hall, Upper saddle Rivers, New Jersey, 2001. Elad, Aharon (bib16) 2006; 15 . Pizer, Amburn, Austin, Cromartie, Geselowitz, Greer, ter Haar Romeny, Zimmerman, Zuiderveld (bib9) 1987; 39 Jain, Seung (bib19) 2008 F. Agostinelli, M.R. Anderson, H. Lee, Adaptive multi-column deep neural networks with application to robust image denoising, in: Advances in Neural Information Processing Systems, 2013, pp. 1493–1501. H.C. Burger, C.J. Schuler, S. Harmeling, Image denoising: Can plain neural networks compete with bm3d? in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Providence, RI, 2012, pp. 2392–2399. Loza, Bull, Hill, Achim (bib6) 2013; 23 Wang, Bovik, Sheik, Simoncelli (bib27) 2004; 13 R. Krutsch, D. Tenorlo, Histogram equalization, Application Note AN4318, Freescale Semiconductors Inc, June 2011. K. Fotiadou, G. Tsagkatakis, P. Tsakalides, Low light image enhancement via sparse representations, in: Image Analysis and Recognition, Springer, Vilamoura, Algarve, Portugal, 2014, pp. 84–93. X. Zhang, P. Shen, L. Luo, L. Zhang, J. Song, Enhancement and noise reduction of very low light level images, in: 2012 21st International Conference on Pattern Recognition (ICPR), IEEE, Tsukuba Science City, Japan, 2012, pp. 2034–2037. Cheng, Shi (bib29) 2004; 14 J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, Y. Bengio, Theano: a CPU and GPUmath expression compiler, in: Proceedings of the Python for Scientific Computing Conference (SciPy), 2010, oral Presentation. Cheng (10.1016/j.patcog.2016.06.008_bib29) 2004; 14 Wu (10.1016/j.patcog.2016.06.008_bib13) 2011; 20 Jain (10.1016/j.patcog.2016.06.008_bib19) 2008 10.1016/j.patcog.2016.06.008_bib28 Pizer (10.1016/j.patcog.2016.06.008_bib9) 1987; 39 10.1016/j.patcog.2016.06.008_bib25 10.1016/j.patcog.2016.06.008_bib24 10.1016/j.patcog.2016.06.008_bib23 10.1016/j.patcog.2016.06.008_bib22 10.1016/j.patcog.2016.06.008_bib21 Dabov (10.1016/j.patcog.2016.06.008_bib15) 2007; 16 10.1016/j.patcog.2016.06.008_bib31 10.1016/j.patcog.2016.06.008_bib30 Kaur (10.1016/j.patcog.2016.06.008_bib12) 2011; 2 Wang (10.1016/j.patcog.2016.06.008_bib27) 2004; 13 Elad (10.1016/j.patcog.2016.06.008_bib16) 2006; 15 10.1016/j.patcog.2016.06.008_bib18 Santoso (10.1016/j.patcog.2016.06.008_bib26) 2011; 2 10.1016/j.patcog.2016.06.008_bib14 Bergstra (10.1016/j.patcog.2016.06.008_bib32) 2012; 13 10.1016/j.patcog.2016.06.008_bib11 10.1016/j.patcog.2016.06.008_bib4 10.1016/j.patcog.2016.06.008_bib20 10.1016/j.patcog.2016.06.008_bib3 10.1016/j.patcog.2016.06.008_bib2 10.1016/j.patcog.2016.06.008_bib1 10.1016/j.patcog.2016.06.008_bib8 10.1016/j.patcog.2016.06.008_bib7 Pisano (10.1016/j.patcog.2016.06.008_bib10) 1998; 11 10.1016/j.patcog.2016.06.008_bib5 Loza (10.1016/j.patcog.2016.06.008_bib6) 2013; 23 Chan (10.1016/j.patcog.2016.06.008_bib17) 2005; 14
References_xml	– volume: 16 start-page: 2080 year: 2007 end-page: 2095 ident: bib15 article-title: Image denoising by sparse 3-d transform-domain collaborative filtering publication-title: IEEE Trans. Image Process. – reference: Y. LeCun, F.J. Huang, L. Bottou, Learning methods for generic object recognition with invariance to pose and lighting, in: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, vol. 2, IEEE, Washington, DC.,2004, pp. II–97. – reference: X. Dong, G. Wang, Y.A. Pang, W. Li, J.G. Wen, W. Meng, Y. Lu, Fast efficient algorithm for enhancement of low lighting video, in: 2011 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Barcelona, 2011, pp. 1–6. – reference: A. Yamasaki, H. Takauji, S. Kaneko, T. Kanade, H. Ohki, Denighting: Enhancement of nighttime images for a surveillance camera, in: 19th International Conference on Pattern Recognition, 2008. ICPR 2008, IEEE, Tampa, FL, 2008, pp. 1–4. – reference: X. Zhang, P. Shen, L. Luo, L. Zhang, J. Song, Enhancement and noise reduction of very low light level images, in: 2012 21st International Conference on Pattern Recognition (ICPR), IEEE, Tsukuba Science City, Japan, 2012, pp. 2034–2037. – start-page: 1 year: 2008 end-page: 8 ident: bib19 article-title: Natural image denoising with convolutional networks publication-title: Neural Inf. Process. Stand. – volume: 13 start-page: 281 year: 2012 end-page: 305 ident: bib32 article-title: Random search for hyper-parameter optimization publication-title: J. Mach. Learn. Res. – reference: P. Trahanias, A. Venetsanopoulos, Color image enhancement through 3-d histogram equalization, in: Proceedings of 11th IAPR International Conference on Pattern Recognition, 1992. Vol. III. Conference C: Image, Speech and Signal Analysis, IEEE, The Hague, The Netherlands, 1992, pp. 545–548. – volume: 15 start-page: 3736 year: 2006 end-page: 3745 ident: bib16 article-title: Image denoising via sparse and redundant representations over learned dictionaries publication-title: IEEE Trans. Image Process. – reference: K. Fotiadou, G. Tsagkatakis, P. Tsakalides, Low light image enhancement via sparse representations, in: Image Analysis and Recognition, Springer, Vilamoura, Algarve, Portugal, 2014, pp. 84–93. – reference: P. Vincent, H. Larochelle, Y. Bengio, Extracting and composing robust features with denoising autoencoders, in: Proceedings of the 25th International conference on Machine Learning-ICML'08, 2008, pp. 1096–1103. – volume: 2 start-page: 137 year: 2011 end-page: 141 ident: bib12 article-title: Survey of contrast enhancement techniques based on histogram equalization publication-title: Int. J. Adv. Comput. Sci. Appl. – reference: C. Couprie, C. Farabet, L. Najman, Y. LeCun, Indoor semantic segmentation using depth information, in: ICLR, 2013. – reference: F. Agostinelli, M.R. Anderson, H. Lee, Adaptive multi-column deep neural networks with application to robust image denoising, in: Advances in Neural Information Processing Systems, 2013, pp. 1493–1501. – volume: 13 start-page: 600 year: 2004 end-page: 612 ident: bib27 article-title: Image quality assessment publication-title: IEE Trans. Image Process. – reference: A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada, 2012. – reference: K.G. Lore, N. Sweet, K. Kumar, N. Ahmed, S. Sarkar, Deep value of information estimators for collaborative human–machine information gathering, in: International Conference of Cyber-physical Systems (ICCPS), Vienna, Austria, 2016. – reference: J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, Y. Bengio, Theano: a CPU and GPUmath expression compiler, in: Proceedings of the Python for Scientific Computing Conference (SciPy), 2010, oral Presentation. – volume: 14 start-page: 1479 year: 2005 end-page: 1485 ident: bib17 article-title: Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization publication-title: IEEE Trans. Image Process. – reference: . Paper PDF is found here: – reference: R. Krutsch, D. Tenorlo, Histogram equalization, Application Note AN4318, Freescale Semiconductors Inc, June 2011. – reference: J. Xie, L. Xu, E. Chen, Image denoising and inpainting with deep neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 341–349. – volume: 39 start-page: 355 year: 1987 end-page: 368 ident: bib9 article-title: Adaptive histogram equalization and its variations publication-title: Comput. Vis. Graph. Image Process. – volume: 14 start-page: 158 year: 2004 end-page: 170 ident: bib29 article-title: A simple and effective histogram equalization approach to image enhancement publication-title: Digit. Signal Process. – reference: S. Sarkar, K.G. Lore, S. Sarkar, V. Ramanan, S.R. Chakravarthy, S. Phoha, A. Ray, Early detection of combustion instability from hi-speed flame images via deep learning and symbolic time series analysis, Proceedings of the Annual Conference of the Prognostics and Health Management Society, Coronado, California, 2015, – reference: . – reference: S. Sarkar, V. Venugopalan, K. Reddy, J. Ryde, M. Giering, N. Jaitly, Occlusion edge detection in rgbd frames using deep convolutional neural networks, in: Proceedings of IEEE High Performance Extreme Computing Conference, Waltham, MA, 2015. – volume: 20 start-page: 1262 year: 2011 end-page: 1272 ident: bib13 article-title: A linear programming approach for optimal contrast-tone mapping publication-title: IEEE Trans. Image Process. – volume: 23 start-page: 1856 year: 2013 end-page: 1866 ident: bib6 article-title: Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients publication-title: Digit. Signal Process. – volume: 2 start-page: 1 year: 2011 end-page: 10 ident: bib26 article-title: Compression ratio and peak signal to noise ratio in grayscale image compression using wavelet publication-title: Int. J. Comput. Sci. Technol. – reference: H.C. Burger, C.J. Schuler, S. Harmeling, Image denoising: Can plain neural networks compete with bm3d? in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Providence, RI, 2012, pp. 2392–2399. – reference: F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron, N. Bouchard, Y. Bengio, Theano: new features and speed improvements, in: Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012. – volume: 11 start-page: 193 year: 1998 end-page: 200 ident: bib10 article-title: Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms publication-title: J. Digit. Imaging – reference: R. Gonzalex, R. Woods, Digital image Processing, 2nd Edition, no. 0-201-28075-8 in 0-201-28075-8, Prentice Hall, Upper saddle Rivers, New Jersey, 2001. – ident: 10.1016/j.patcog.2016.06.008_bib30 – ident: 10.1016/j.patcog.2016.06.008_bib22 doi: 10.1007/978-3-319-11758-4_10 – volume: 2 start-page: 1 issue: 2 year: 2011 ident: 10.1016/j.patcog.2016.06.008_bib26 article-title: Compression ratio and peak signal to noise ratio in grayscale image compression using wavelet publication-title: Int. J. Comput. Sci. Technol. – ident: 10.1016/j.patcog.2016.06.008_bib21 doi: 10.1109/CVPR.2012.6247952 – volume: 2 start-page: 137 issue: 7 year: 2011 ident: 10.1016/j.patcog.2016.06.008_bib12 article-title: Survey of contrast enhancement techniques based on histogram equalization publication-title: Int. J. Adv. Comput. Sci. Appl. – ident: 10.1016/j.patcog.2016.06.008_bib5 doi: 10.1109/ICCPS.2016.7479095 – ident: 10.1016/j.patcog.2016.06.008_bib1 – volume: 15 start-page: 3736 issue: 12 year: 2006 ident: 10.1016/j.patcog.2016.06.008_bib16 article-title: Image denoising via sparse and redundant representations over learned dictionaries publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2006.881969 – volume: 23 start-page: 1856 issue: 6 year: 2013 ident: 10.1016/j.patcog.2016.06.008_bib6 article-title: Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients publication-title: Digit. Signal Process. doi: 10.1016/j.dsp.2013.06.002 – volume: 14 start-page: 158 issue: 2 year: 2004 ident: 10.1016/j.patcog.2016.06.008_bib29 article-title: A simple and effective histogram equalization approach to image enhancement publication-title: Digit. Signal Process. doi: 10.1016/j.dsp.2003.07.002 – volume: 13 start-page: 281 issue: 1 year: 2012 ident: 10.1016/j.patcog.2016.06.008_bib32 article-title: Random search for hyper-parameter optimization publication-title: J. Mach. Learn. Res. – volume: 39 start-page: 355 issue: 3 year: 1987 ident: 10.1016/j.patcog.2016.06.008_bib9 article-title: Adaptive histogram equalization and its variations publication-title: Comput. Vis. Graph. Image Process. doi: 10.1016/S0734-189X(87)80186-X – volume: 20 start-page: 1262 issue: 5 year: 2011 ident: 10.1016/j.patcog.2016.06.008_bib13 article-title: A linear programming approach for optimal contrast-tone mapping publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2010.2092438 – ident: 10.1016/j.patcog.2016.06.008_bib11 – start-page: 1 year: 2008 ident: 10.1016/j.patcog.2016.06.008_bib19 article-title: Natural image denoising with convolutional networks publication-title: Neural Inf. Process. Stand. – ident: 10.1016/j.patcog.2016.06.008_bib4 doi: 10.36001/phmconf.2015.v7i1.2723 – ident: 10.1016/j.patcog.2016.06.008_bib28 doi: 10.1109/ICPR.1992.202045 – ident: 10.1016/j.patcog.2016.06.008_bib25 – volume: 13 start-page: 600 issue: 4 year: 2004 ident: 10.1016/j.patcog.2016.06.008_bib27 article-title: Image quality assessment publication-title: IEE Trans. Image Process. doi: 10.1109/TIP.2003.819861 – volume: 16 start-page: 2080 issue: 8 year: 2007 ident: 10.1016/j.patcog.2016.06.008_bib15 article-title: Image denoising by sparse 3-d transform-domain collaborative filtering publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2007.901238 – ident: 10.1016/j.patcog.2016.06.008_bib23 – ident: 10.1016/j.patcog.2016.06.008_bib7 doi: 10.1109/ICME.2011.6012107 – ident: 10.1016/j.patcog.2016.06.008_bib2 – ident: 10.1016/j.patcog.2016.06.008_bib24 doi: 10.1109/CVPR.2004.1315150 – volume: 14 start-page: 1479 issue: 10 year: 2005 ident: 10.1016/j.patcog.2016.06.008_bib17 article-title: Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2005.852196 – ident: 10.1016/j.patcog.2016.06.008_bib20 – ident: 10.1016/j.patcog.2016.06.008_bib18 doi: 10.1145/1390156.1390294 – ident: 10.1016/j.patcog.2016.06.008_bib31 doi: 10.25080/Majora-92bf1922-003 – ident: 10.1016/j.patcog.2016.06.008_bib3 doi: 10.1007/s11265-016-1209-3 – ident: 10.1016/j.patcog.2016.06.008_bib8 doi: 10.1109/ICPR.2008.4761424 – ident: 10.1016/j.patcog.2016.06.008_bib14 – volume: 11 start-page: 193 issue: 4 year: 1998 ident: 10.1016/j.patcog.2016.06.008_bib10 article-title: Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms publication-title: J. Digit. Imaging doi: 10.1007/BF03178082
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Title	LLNet: A deep autoencoder approach to natural low-light image enhancement
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