Soft Computing

, Volume 22, Issue 5, pp 1399–1420 | Cite as

Retinex-based image enhancement framework by using region covariance filter

  • Fuyu Tao
  • Xiaomin Yang
  • Wei Wu
  • Kai Liu
  • Zhili Zhou
  • Yiguang Liu
Focus
First Online:
  • 223 Downloads

Abstract

Clear images are critical in understanding real scenarios. However, the quality of images may be severely declined due to terrible conditions. Images exposed to such conditions are usually of low contrast, contain much noise, and suffer from weak details. And these drawbacks tend to negatively influence the subsequent processing tasks. Many existing image enhancement methods only solve a certain aspect of aforementioned drawbacks. This paper proposes a Retinex-based image enhancement framework that can increase contrast, eliminate noise, and enhance details at the same time. First, we utilize a region covariance filter to estimate the illumination accurately at multiple scales. The corresponding reflectance can be predicted by dividing the original image by its illumination. Second, we utilize contrast-limited adaptive histogram equalization to enhance the global contrast of original images because the illumination contains the low-frequency component. Third, since the reflectance contains the details of the original image and noise, we adopt a non-local means filter to eliminate noise and use a guided filter to enhance the details in the reflectance. Fourth, we synthesize the final enhanced image by fusing the enhanced illumination and reflectance at each scale. Experiments have proved the improvement of the proposed framework in terms of both visual perception and quantitative comparisons with other compared methods.

Keywords

Retinex Image enhancement Multiple scales Illumination estimation 
This is a preview of subscription content, log in to check access

Notes

Acknowledgements

The study was funded by the National Natural Science Foundation of China (No. 61711540303), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) Fund, and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET) Fund.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

References

  1. Ahn H, Keum B, Kim D, Lee HS (2013) Adaptive local tone mapping based on retinex for high dynamic range images. In: IEEE international conference on consumer electronics, pp 153–156Google Scholar
  2. Alspach DL, Sorenson HW (1972) Nonlinear Bayesian estimation using Gaussian sum approximations. IEEE Trans Autom Control 17(4):439–448CrossRefMATHGoogle Scholar
  3. Asha M, Gupta MK (2016) A basic approach to enhance a gray scale image. Imp J Interdiscip Res 2(7):126–129Google Scholar
  4. Bai X, Zhou F, Xue B (2011) Infrared image enhancement through contrast enhancement by using multiscale new top-hat transform. Infrared Phys Technol 54(54):61–69CrossRefGoogle Scholar
  5. Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. In: IEEE computer society conference on computer vision and pattern recognition, pp 60–65Google Scholar
  6. Chen B, Shu H, Coatrieux G, Chen G, Sun X, Coatrieux JL (2014) Color image analysis by quaternion-type moments. J Math Imaging Vis 51(1):124–144MathSciNetCrossRefMATHGoogle Scholar
  7. Cheng H, Zhang Y (2012) Detecting of contrast over-enhancement. In: 2012 19th IEEE international conference on image processing, pp 961–964. IEEEGoogle Scholar
  8. Choudhury A, Medioni G (2009) Perceptually motivated automatic color contrast enhancement. In: ICCV 2009—CRICV workshop, vol 7525, no 1, pp 1893–1900Google Scholar
  9. Elad M (2005) Retinex by two bilateral filters. Springer, BerlinGoogle Scholar
  10. Fallah M, Azizi A (2010) Quality assessment of image fusion techniques for multisensor high resolution satellite images (case study: IRS-p5 and IRS-P6 satellite images). Years Isprs Adv Remote Sensingence Pt 38(1):204–209Google Scholar
  11. Funt B, Ciurea F, Mccann J (2004) Retinex in matlab? J Electron Imaging 13(1):48–57CrossRefGoogle Scholar
  12. Ghani ASA, Isa NAM (2015) Enhancement of low quality underwater image through integrated global and local contrast correction. Appl Soft Comput 37(C):332–344CrossRefGoogle Scholar
  13. Gu K, Zhai G, Yang X, Zhang W (2014) Automatic contrast enhancement technology with saliency preservation. IEEE Trans Circuits Syst Video Technol 25(9):1–1Google Scholar
  14. He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409CrossRefGoogle Scholar
  15. Jobson DJ, Rahman Z, Woodell GA (1997) Properties and performance of a center/surround retinex. IEEE Trans Image Process A Publ IEEE Signal Process Soc 6(3):451–462CrossRefGoogle Scholar
  16. Jobson DJ, Rahman ZU, Woodell GA (1997) A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans Image Process 6(7):965–976CrossRefGoogle Scholar
  17. Karacan L, Erdem E, Erdem A (2013) Structure-preserving image smoothing via region covariances. ACM Trans Graph 32(6):1–11CrossRefGoogle Scholar
  18. Khan NA, Sandsten M (2016) Time–frequency image enhancement based on interference suppression in Wigner–Ville distribution. Signal Process 127:80–85CrossRefGoogle Scholar
  19. Kim SE, Jeon JJ, Eom IK (2016) Image contrast enhancement using entropy scaling in wavelet domain. Signal Process 127:1–11CrossRefGoogle Scholar
  20. Kimmel R, Elad M, Shaked D, Keshet R, Sobel I (2003) A variational framework for retinex. Int J Comput Vis 52(1):7–23CrossRefMATHGoogle Scholar
  21. Lai R, Yang YT, Wang BJ, Zhou HX (2010) A quantitative measure based infrared image enhancement algorithm using plateau histogram. Opt Commun 283(21):4283–4288CrossRefGoogle Scholar
  22. Land EH, Mccann JJ (1971) Lightness and retinex theory. J Opt Soc Am 61(1):1–11CrossRefGoogle Scholar
  23. Li B, Xie W (2016) Image denoising and enhancement based on adaptive fractional calculus of small probability strategy. Neurocomputing 175:704–714CrossRefGoogle Scholar
  24. Liu N, Zhang Y, Xie J, Yu J, Xiao H, Min T (2015) A novel high dynamic range image enhancement algorithm based on guided image filter. Opt Int J Light Electron Opt 126(23):4581–4585CrossRefGoogle Scholar
  25. Maini R, Aggarwal H (2010) A comprehensive review of image enhancement techniques. Comput Sci 2(3):8–13Google Scholar
  26. Meylan L, Susstrunk S (2006) High dynamic range image rendering with a retinex-based adaptive filter. IEEE Trans Image Process A Publ IEEE Signal Process Soc 15(9):2820–2830CrossRefGoogle Scholar
  27. Narsimha B, Suresh E, Chandar KP, Komuraiah B (2010) Enhancement of color images in hot domain with quantitative measurements using entropy and relative entropy In: International conference on signal and image processing, pp 34–38Google Scholar
  28. Nercessian SC, Panetta KA, Agaian SS (2013) Non-linear direct multi-scale image enhancement based on the luminance and contrast masking characteristics of the human visual system. IEEE Trans Image Process A Publ IEEE Signal Process Soc 22(9):3549–3561CrossRefGoogle Scholar
  29. Pan Z, Lei J, Zhang Y, Sun X, Kwong S (2016) Fast motion estimation based on content property for low-complexity h. 265/hevc encoder. IEEE Trans Broadcast. 62(3):675–684. doi: 10.1109/TBC.2016.2580920
  30. Paris S, Hasinoff SW, Kautz J (2011) Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. ACM Trans Graph 30(4, article 68):1244–1259Google Scholar
  31. Qi W, Han J, Zhang Y, Bai LF (2016) Hierarchical image enhancement. Infrared Phys Technol 76:704–709CrossRefGoogle Scholar
  32. Rahman ZU, Jobson DJ, Woodell GA (1996) Multi-scale retinex for color image enhancement. In: Proceedings of the international conference on image processing, 1996, vol 3, pp 1003–1006Google Scholar
  33. Starck JL, Murtagh F, Cands EJ, Donoho DL (2003) Gray and color image contrast enhancement by the curvelet transform. IEEE Trans Image Process A Publ IEEE Signal Process Soc 12(6):706–717MathSciNetCrossRefMATHGoogle Scholar
  34. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: International conference on computer vision, pp 839–846Google Scholar
  35. Tuzel O, Porikli F, Meer P (2006) Region covariance: a fast descriptor for detection and classification. In: Proceedings of the computer vision—ECCV 2006, European conference on computer vision, Graz, Austria, May 7–13, 2006, pp 589–600Google Scholar
  36. Wang Y, Wang H, Yin C, Dai M (2016) Biologically inspired image enhancement based on retinex. Neurocomputing 177:373–384CrossRefGoogle Scholar
  37. Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406CrossRefGoogle Scholar
  38. Xia Z, Wang X, Zhang L, Qin Z, Sun X, Ren K (2016) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Forensics Secur 11(11):2594–2608Google Scholar
  39. Xiao J, Peng H, Zhang Y, Tu C, Li Q (2014) Fast image enhancement based on color space fusion. Color Res Appl 41(1):22–31CrossRefGoogle Scholar
  40. Xu L, Lu C, Xu Y, Jia J (2011) Image smoothing via L0 gradient minimization. ACM Trans Graph 30(6):61–64Google Scholar
  41. Xu L, Yan Q, Xia Y, Jia J (2012) Structure extraction from texture via relative total variation. ACM Trans Graph 31(6):439–445Google Scholar
  42. Yuan T, Zheng X, Hu X, Zhou W, Wang W (2013) A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm. Linear Algebra Appl 244(3):69–80Google Scholar
  43. Yuan C, Sun X, Lv R (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Commun 13(7):60–65CrossRefGoogle Scholar
  44. Zhang Q, Shen X, Xu L, Jia J (2014) Rolling guidance filter. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8691. Springer, ChamGoogle Scholar
  45. Zheng Y, Byeungwoo J, Xu D, Wu QMJ, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy c-means algorithm. J Intell Fuzzy Syst 28(2):4024–4028Google Scholar
  46. Zhou Z, Wang Y, Wu QJ, Yang CN, Sun X (2016) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensics Secur 12:48–63CrossRefGoogle Scholar
  47. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Graphics gems IV. Academic Press Professional, pp 474–485Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Fuyu Tao
    • 1
  • Xiaomin Yang
    • 1
  • Wei Wu
    • 1
  • Kai Liu
    • 2
  • Zhili Zhou
    • 3
  • Yiguang Liu
    • 4
  1. 1.Sichuan UniversityCollege of Electronics and Information EngineeringChengduChina
  2. 2.Sichuan UniversitySchool of Electrical Engineering and InformationChengduChina
  3. 3.Jiangsu Engineering Center of Network Monitoring and School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  4. 4.Sichuan UniversitySchool of Computer ScienceChengduChina

Personalised recommendations