Open Access
Volume 40, Number 6, December 2022
Page(s) 1327 - 1334
Published online 10 February 2023
  1. LIU Weidong, LI Jiyu, ZHANG Wenbo, et al. Underwater image enhancement method with non-uniform illumination based on Retinex and ADMM[J]. Journal of Northwestern Polytechnical University, 2021, 39(4): 824–830. [Article] (in Chinese) [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  2. DUAN Biao, LI Jing, Chen Huaimin, et al. New approach to dehaze single nighttime image[J]. Journal of Northwestern Polytechnical University, 2021, 39(3): 604–610. [Article] (in Chinese) [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  3. XIAO Jinsheng, PANG Guanlin, TANG Lumin, et al. Image texture enhancement supersampling algorithm based on contour template and self-learning[J]. Acta Automatica Sinica, 2016, 42(8): 1248–1258. [Article] (in Chinese) [Google Scholar]
  4. XU Shaoping, ZHANG Guizhen, LIN Zhenyu, et al. A low-light image enhancement algorithm based on local structured fusion of multiple images[J]. Acta Automatica Sinica, 2022, 48(10): 1–15. [Article] (in Chinese) [Google Scholar]
  5. XU H T, ZHAI G T, WU X L, et al. Generalized equal-ization model for image enhancement[J]. IEEE Trans on Multimedia, 2014, 16(1): 68–82. [Article] [CrossRef] [Google Scholar]
  6. CELIK T. Spatial entropy-based global and local image con-trast enhancement[J]. IEEE Trans on Image Processing, 2014, 23(12): 5298–5308. [Article] [CrossRef] [Google Scholar]
  7. LIU Zhicheng, WANG Dianwei, LIU Ying, et al. Adaptive correction algorithm for uneven illumination image based on two-dimensional gamma function[J]. Journal of Beijing Institute of Technology, 2016, 36(2): 191–196. [Article] (in Chinese) [Google Scholar]
  8. 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]
  9. JOBSON D J, RAHMAN Z, WOODELL G A. A multiscale retinex for bridging the gap between color images and the human observation of scenes[J]. IEEE Trans on Image Processing, 1997, 6(7): 965–976. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  10. WANG Y K, HUANG W B. A CUDA-enabled parallel algorithm for accelerating retinex[J]. Journal of Real-Time Image Processing, 2014, 9(3): 407–425. [Article] [CrossRef] [Google Scholar]
  11. HENG Baochuan, XIAO Di, ZHANG Xiang. Night-time color image stitching algorithm combined with MSRCP enhancement[J]. Computer Engineering and Design, 2019, 40(11): 3200–3204. [Article] (in Chinese) [Google Scholar]
  12. JI W, LIU D, MENG Y. Exploring the solutions via Retinex enhancements for fruit recognition impacts of outdoor sunlight: a case study of navel oranges[J]. Evolutionary Intelligence, 2022, 15(3): 1875–1911. [Article] [CrossRef] [Google Scholar]
  13. GUO X J, LI Y, LING H B. LIME: low-light image enhance-ment via illumination map estimation[J]. IEEE Trans on Image Processing, 2017, 26(2): 982–993. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  14. DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3D transform-domain collaborative filtering[J]. IEEE Trans on Image Processing, 2007, 16(8): 2080–2095. [Article] [CrossRef] [Google Scholar]
  15. DONG X, WANG G, Pang T, et al. Fast efficient algorithm for enhancement of low lighting video[C]//Proceedings of IEEE & International Conference on Multimedia and Expo, 2011 [Google Scholar]
  16. HE K, SUN J, TANG X. Single image haze removal using dark channel prior[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2010, 33(12): 2341–2353 [Google Scholar]
  17. LI Z G, ZHENG J H, RAHARDJA S. Detail-enhanced exposure fusion[J]. IEEE Trans on Image Processing, 2012, 21(11): 4672–4676. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  18. FU X, ZENG D, HUANG Y, et al. A fusion-based enhancing method for weakly illuminated images[J]. Signal Processing, 2016, 129: 82–96. [Article] [CrossRef] [Google Scholar]
  19. YING Z, GE L, WEN G. A bio-inspired multi-exposure fusion framework for low-light image enhancement[J/OL]. (2017-11-02)[2022-01-04]. [Article] [Google Scholar]
  20. YING Z, GE L, REN Y, et al. A new image contrast enhancement algorithm using exposure fusion framework[C]//International Conference on Computer Analysis of Images and Patterns, 2017 [Google Scholar]
  21. LIU S, ZHANG Y. Detail-preserving underexposed image enhancement via optimal weighted multi-exposure fusion[J]. IEEE Trans on Consumer Electronics, 2019, 65(3): 303–311 [CrossRef] [Google Scholar]
  22. KIM Y, KOH Y J, LEE C, et al. Dark image enhancement based onpairwise target contrast and multi-scale detail boosting[C]//IEEE International Conference on Image Processing, 2015 [Google Scholar]
  23. ZHANG Q, NIE Y, ZHENG W S. Dual illumination estimation for robust exposure correction[C]//Computer Graphics Forum, 2019 [Google Scholar]
  24. WANG S, ZHENG J, HU H, 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]
  25. Fu X, Zeng D, Huang Y, et al. A weighted variational model for simultaneous reflectance and illumination estimation[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016 [Google Scholar]
  26. WANG W, CHEN Z, YUAN X, et al. Adaptive image enhancement method for correcting low-illumination images[J]. Information Sciences, 2019, 496: 25–41 [CrossRef] [Google Scholar]
  27. HAO S, HAN X, GUO Y, et al. Low-light image enhancement with semi-decoupled decomposition[J]. IEEE Trans on Multimedia, 2020, 22(12): 3025–3038 [CrossRef] [Google Scholar]
  28. LI M, LIU J, YANG W, et al. Structure-revealing low-light image enhancement via robust retinex model[J]. IEEE Trans on Image Processing, 2018, 27(6): 2828–2841. [Article] [CrossRef] [Google Scholar]
  29. ZHANG L, ZHANG L, BOVIK A C. A feature-enriched completely blind image quality evaluator[J]. IEEE Trans on Image Processing, 2015, 24(8): 2579–2591. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  30. MIN X, ZHAI G, GU K, et al. Blind image quality estimation via distortion aggravation[J]. IEEE Trans on Broadcasting, 2018, 64(2): 508–517. [Article] [CrossRef] [Google Scholar]
  31. LIU L, LIU B, HUANG H, et al. No-reference image quality assessment based on spatial and spectral entropies[J]. Signal Processing Image Communication, 2014, 29(8): 856–863. [Article] [CrossRef] [Google Scholar]
  32. VENKATANATH N, PRANEETH D, CHANDRASEKHAR B, et al. Blind image quality evaluation using perception based features[C]//2015 21st National Conference on Communications, 2015 [Google Scholar]
  33. MITTAL A, SOUNDARARAJAN R, BOVIK A C. Making a "completely blind" image quality analyzer[J]. IEEE Trans on Signal Processing Letters, 2012, 20(3): 209–212. [Article] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.