Abstract
This paper proposes a novel enhancement approach using Z-score for images captured under hazy and non-uniform illumination conditions for multimedia applications. The proposed approach aims to estimate the scene transmission using Z-score-based weighting function and global atmospheric light for image dehazing. On the contrary, the proposed approach equalizes the illumination channel using Z-score weighting function of non-uniformly illuminated image. The comparative analysis to show the effectiveness of the proposed approach is also presented quantitatively and visually. The datasets used for comparison are realistic single image dehazing dataset, high dynamic range dataset, images captured with commercial DIgital CaMeras, MiddleBury Stereo dataset, and natural benchmarked images. The dehazed images are compared in terms of peak signal to noise ratio, structural similarity index, Lightness Order Error (LOE), and Naturalness Image Quality Evaluator (NIQE). The enhanced version of non-uniformly illuminated images is compared in terms of LOE and NIQE performance measures. The comparison shows that the proposed approach outperforms others.












Similar content being viewed by others
References
Huang SC, Chen BH, Cheng YJ. An efficient visibility enhancement algorithm for road scenes captured by intelligent transportation systems. IEEE Trans Intell Transport Syst. 2014;15(5):2321–32.
Shih K-T, Chen H-H. Exploiting perceptual anchoring for color image enhancement. IEEE Trans Multimed. 2016;18(2):300–10.
Long J, Shi Z, Tang W, Zhang C. Single remote sensing image dehazing. IEEE Geosci Remote Sens Lett. 2014;11(1):59–63.
Chaudhry AM, Riaz MM, Ghafoor A. A framework for outdoor RGB image enhancement and dehazing. IEEE Geosci Remote Sens Lett. 2018;15(6):932–6.
Zhang X, Liu L, Chen X, Xie S, Lei L. A novel multitemporal cloud and cloud shadow detection method using the integrated cloud Z-scores model. IEEE J Sel Top Appl Earth Observ Remote Sens. 2019;12(1):123–34.
Makarau A, Richter R, Müller R, Reinartz P. Haze detection and removal in remotely sensed multispectral imagery. IEEE Trans Geosci Remote Sens. 2014;52(9):5895–905.
Makarau A, Richter R, Schlöpfer D, Reinartz P. Combined haze and cirrus removal for multispectral imagery. IEEE Geosci Remote Sens Lett. 2016;13(3):379–83.
Nayar SK, Narasimhan SG. Vision in bad weather. In: Proceedings of the Seventh IEEE International Conference on Computer Vision 1999 Sep 20 (Vol. 2, pp. 820–7). IEEE.
Land EH, McCann JJ. Lightness and Retinex theory. J Opt Soc Am. 1971;61(1):1–11.
He K, Sun J, Tang X. Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell. 2011;33(12):2341–53.
Gao Y, Hu H-M, Li B, Guo Q. Naturalness preserved nonuniform illumination estimation for image enhancement based on Retinex. IEEE Trans Multimed. 2018;20(2):335–44.
Schechner YY, Narasimhan SG, Nayar SK. Instant dehazing of images using polarization. In: IEEE Conf. on Comp. Vision and Pattern Recog. Kauai, HI: USA; 2001. vol. 1, pp. I-325–I-32.
Schechner YY, Narasimhan SG, Nayar SK. Polarization-based vision through haze. Appl Opt. 2003;42(3):511–25.
Narasimhan SG, Nayar SK. Chromatic framework for vision in bad weather. In: IEEE Conf. on comp. vision and pattern recog. Hilton Head Island, SC: USA; 2000. vol. 1, p. 598–605.
Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, Romeny BTH, Zimmerman JB. Adaptive histogram equalization and its variations. Comp Vis Graphics Image Process. 1987;39(3):355–68.
Zuiderveld K. Contrast limited adaptive histogram equalization. In: Graphics Gems IV. 1994;474–85. https://doi.org/10.5555/180895.180940.
Meng G, Wang Y, Duan J, Xiang S, Pan C. Efficient image dehazing with boundary constraint and contextual regularization. In: IEEE Int. Conf. on comp. vision, Sydney, NSW, Australia, Dec. 1–8, 2013, pp. 617–24.
Celik T, Tjahjadi T. Contextual and variational contrast enhancement. IEEE Trans Image Process. 2011;20(12):3431–41.
Lee C, Lee C, Kim C-S. Contrast enhancement based on layered difference representation of 2D histograms. IEEE Trans Image Process. 2013;22(12):5372–84.
Jobson DJ, Rahman Z, Woodell GA. Properties and performance of a center/surround Retinex. IEEE Trans Image Process. 1996;6(3):451–62.
Rahman Z, Jobson DJ, Woodell GA. Multi-scale Retinex for color image enhancement. In: IEEE Int. Conf. on image processing, Lausanne, Switzerland, Sept. 19, 1996, vol. 3, pp. 1003–6.
Fu X, Zeng D, Huang Y, Zhang X, Ding X, A weighted variational model for simultaneous reflectance and illumination estimation. In: IEEE Conf. on comp. vision and pattern recog., Las Vegas, NV, USA, June 27–30, 2016, pp. 2782–90.
Shin Y, Jeong S, Lee S. Efficient naturalness restoration for non uniform illumination images. IET Image Process J. 2015;9(8):662–71.
Guo X, Li Y, Ling H. LIME: low-light image enhancement via illumination map estimation. IEEE Trans Image Process. 2017;26(2):982–93.
Ghosh S, Gavaskar RG, Panda D, Chaudhury KN. Fast scale-adaptive bilateral texture smoothing. IEEE Trans Circ Syst Video Technol. 2020;30(7):2015–26.
Ghosh S, Gavaskar RG, Chaudhury KM. Saliency guided image detail enhancement. In: 2019 National Conference on Communications (NCC), Bangalore, India, Feb. 20–3, 2019.
Ren Y, Ying Z, Li TH, Li G. LECARM: low-light image enhancement using the camera response model. IEEE Trans Circ and Syst for Video Tech. 2019;29(4):968–81.
Zhu Q, Mai J, Shao L. A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process. 2015;24(11):3522–33.
Cai B, Xu X, Jia K, Qing C, Tao D. DehazeNet: an end-to-end system for single image haze removal. IEEE Trans Image Process. 2016;25(11):5187–98.
Ren W, Liu S, Zhang H, Pan J, Cao X, Yang M-H. Single image dehazing via multi-scale convolutional neural networks. In: European Conf. on Comp. Vision. 2016. pp. 154–69.
Li B, Peng X, Wang Z, Xu J, Feng D. Aod-net: all-in-one dehazing network. In: IEEE Int. Conf. on comp. vision. Venice: Italy; Oct. 22–29, 2017. pp. 4770–78.
Ren W, Ma L, Zhang J, Pan J, Cao X, Liu W, Yang M-H. Gated fusion network for single image dehazing. In: IEEE/CVF Conf. on comp. vision and pattern recog. Salt Lake City: UT, USA; 2018. p. 18–23.
Wang A, Wang W, Liu J, Gu N. AIPNet: image-to-image single image dehazing with atmospheric illumination prior. IEEE Trans Image Process. 2019;28(1):381–93.
Liu Z, Xiao B, Alrabeiah M, Wang K, Chen J. Single image dehazing with a generic model-agnostic convolutional neural network. IEEE Signal Process Lett. 2019;26(6):833–7.
Guo C, Li C, Guo J, Loy CC, Hou J, Kwong S, Cong R. Zero-reference deep curve estimation for low-light image enhancement. In: IEEE Conf. on comp. vision and pattern recog. 2020. p. 1780–89.
Li X, Shen H, Zhang L, Zhang H, Yuan Q, Yang G. Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning. IEEE Trans Geosci Remote Sens. 2014;52(11):7086–98.
Gao Y, Liu G, Ma C. Dense hazy image enhancement based on generalized imaging model. In: 2018 IEEE 3rd International Conference on image, vision and computing (ICIVC). Chongqing: China; June 27–29, 2018, pp. 410–14.
Chandrasekharan R, Sasikumar M. Fuzzy transform for contrast enhancement of nonuniform illumination images. IEEE Signal Process Lett. 2018;25(6):813–7.
Sharma T, Verma NK. Estimating depth and global atmospheric light for image dehazing using Type-2 fuzzy approach. IEEE Trans Emerg Top Comput Intell, 2020; p. 1–10 (Early Access).
Kreyszig E. Advanced engineering mathematics. 4th ed. Hoboken: John Wiley and Sons Ltd; 1979. (ISBN: 0-471-02140-7).
Kranzusch R, Siepen FAD, Wiesemann S, Zange L, Jeuthe S, da Silva TF, Kuehne T, Pieske B, Tillmanns C, Friedrich MG, Schulz-Menger J. Z-score mapping for standardized analysis and reporting of cardiovascular magnetic resonance modified Look-Locker inversion recovery (MOLLI) T1 data: Normal behavior and validation in patients with amyloidosis. J Cardiovasc Magn Reson. 2020;22(1):1–10.
Kim JH, Jang WD, Sim JY, Kim CS. Optimized contrast enhancement for real-time image and video dehazing. J Vis Commun Image Represent. 2013;24(3):410–25.
Salomon D. Data compression: the complete reference. Berlin: Springer-Verlag, New York; 2004.
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13(4):600–12.
Wang S, Zheng J, Hu HM, Li B. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans Image Process. 2013;22(9):3538–48.
Mittal A, Soundararajan R, Bovik AC. Making a completely blind image quality analyzer. IEEE Signal Process Lett. 2013;22(3):209–12.
Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z. Benchmarking single-image dehazing and beyond. IEEE Trans Image Process. 2019;28(1):492–505.
Sen P, Kalantari NK, Yaesoubi M, Darabi S, Goldman DB, Shechtman E. Robust patch-based HDR reconstruction of dynamic scenes. ACM Trans Graphics. 2012;31(6):203:1-203:11.
Scharstein D, Szeliski R. High-accuracy stereo depth maps using structured light. In: IEEE Conf. on Comp. Vision and Pattern Recognition (CVPR), Madison, WI, USA, USA, vol. 1, Jun. 2003, pp. 195–202.
Scharstein D, Pal C. Learning conditional random fields for stereo. In: IEEE Conf. on comp, vision and pattern recognition (CVPR). Minneapolis, MN: USA; 2007, pp. 1–8.
Hirschmüller H, Scharstein D. Evaluation of cost functions for stereo matching. In: : IEEE Conf. on comp, vision and pattern recognition (CVPR). Minneapolis: MN; 2007. pp. 1–8.
Sharma T, Verma NK. Adaptive interval Type-2 fuzzy filter: an AI agent for handling uncertainties to preserve image naturalness. IEEE Trans Artif Intell. 2021;2(1):83–92.
Colores SS, Yepez EC, Arreguin JMR, Botella G, Carrillo LML, Ledesma S. A fast image dehazing algorithm using morphological reconstruction. IEEE Trans Image Process. 2019;28(5):2357–66.
Ghosh S, Chaudhury KN. Fast bright-pass bilateral filtering for low-light enhancement. In: 2019 IEEE International Conference on image processing (ICIP). Taipei: Taiwan; Sept. 22–25, 2019. pp. 205–9.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sharma, T., Verma, N.K. Single Image Dehazing and Non-uniform Illumination Enhancement: A Z-Score Approach. SN COMPUT. SCI. 2, 488 (2021). https://doi.org/10.1007/s42979-021-00912-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-021-00912-1