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All issues Volume 39 / No 4 (August 2021) JNWPU, 39 4 (2021) 824-830 References
Open Access
Issue
JNWPU
Volume 39, Number 4, August 2021
Page(s) 824 - 830
DOI https://doi.org/10.1051/jnwpu/20213940824
Published online 23 September 2021
  1. Li C Y, Guo J C, Cong R M, et al. Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior[J]. IEEE Trans on Image Processing, 2016, 25(12): 5664–5677 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  2. Zhuo Li, Hu Xiaochen, Li Jiafeng, et al. A naturalness-preserved low-light enhancement algorithm for intelligent analysis[J]. Chinese Journal of Electronics, 2019, 28(2): 316–324 [Article] [CrossRef] [Google Scholar]
  3. Li L, Wang R, Wang W, et al. A low-light image enhancement method for both denoising and contrast enlarging[C]//IEEE International Conference on Image Processing, Quebec City, 2015 [Google Scholar]
  4. Wang Y, Huang Q, Hu J. Adaptive enhancement for non-uniform illumination images via pixel-wise histogram modification and color reconstruction[C]//2018 IEEE 3rd International Conference on Signal and Image Processing, Shenzhen, 2018 [Google Scholar]
  5. Wang D, Yan W, Zhu T, et al. An adaptive correction algorithm for non-uniform illumination panoramic images based on the improved bilateral gamma function[C]//2017 International Conference on Digital Image Computing: Techniques and Applications, Sydney, NSW, 2017 [Google Scholar]
  6. Han S, Liu W, Xing W. Image enhancement based on spatial multi-scale homomorphic filtering and local entropy guided image filtering[C]//2017 IEEE 15th International Conterence on Dependable, Autonomic and Secure Computing, 15th International Conference on Pervasive Intelligence and Computing, 3rd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), Orlando, FL, 2017 [Google Scholar]
  7. Wang S H, Zheng J, Hu H M, et al. Naturalness preserved enhancement algorithm for non-uniform illumination images[J]. IEEE Trans on Image Processing, 2013, 22(9): 3538–3548 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  8. Lee S, Kwon H, Han H, et al. A Space-variant luminance map based color image enhancement[J]. IEEE Trans on Consumer Electronics, 2010, 56(4): 2636–2643 [Article] [CrossRef] [Google Scholar]
  9. Gao Y, Hu H M, Li B, et al. Naturalness preserved nonuniform illumination estimation for image enhancement based on retinex[J]. IEEE Trans on Multimedia, 2018, 20(99): 335–344 [Article] [CrossRef] [Google Scholar]
  10. Fu X, Zhuang P, Huang Y, et al. A retinex-based enhancing approach for single underwater image[C]//IEEE International Conference on Image Processing, Paris, 2014 [Google Scholar]
  11. Zhang W, Li G, Ying Z. A New underwater image enhancing method via color correction and illumination adjustment[C]//2017 IEEE Visual Communications and Image Processing, St Petersburg, FL, 2017 [Google Scholar]
  12. Drews J P, Nascimento E, Moraes F, et al. Transmission estimation in underwater single images[C]//IEEE International Conference on Computer Vision Workshops, Sydney, NSW, 2013 [Google Scholar]
  13. Land E H. The retinex theory of color vision[J]. Scientific American, 1977, 237(6): 108–128 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  14. Lee S. An efficient content-based image enhancement in the compressed domain using retinex theory[J]. IEEE Trans on Circuits & Systems for Video Technology, 2007, 17(2): 199–213 [Article] [CrossRef] [Google Scholar]
  15. Patil P D, Kumbhar A D. Bilateral filter for image denoising[C]//International Conference on Green Computing & Internet of Things, Noida, 2015 [Google Scholar]
  16. Lee S, Han H, Kwak B, et al. Color image enhancement method using a space-variant luminance map[C]//Digest of Technical Papers International Conference on Consumer Electronics, 2010 [Google Scholar]
  17. Jobson D J, Rahman Z, Woodell G A. Properties and performance of a center/surround retinex[J]. IEEE Trans on Image Processing, 1997, 6(3): 451–462 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  18. Mittal A, Soundararajan R. Making a 'completely blind' image quality analyzer[J]. IEEE Signal Processing Letters, 2013, 20(3): 209–212 [Article] [NASA ADS] [CrossRef] [Google Scholar]

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