Skip to main content
Log in

Enhancement of IR images using histogram processing and the Undecimated additive wavelet transform

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper presents a fabulous enhancement approach for infrared (IR) images. This approach mixes the benefits of the undecimated Additive Wavelet Transform (AWT) with the homomorphic transform and Contrast Limited Adaptive Histogram Equalization (CLAHE). The basic idea of this approach depends on applying the CLAHE on the IR image. Then, the resultant image is decomposed into sub-bands using the AWT. The homomorphic enhancement is implemented on each sub-band, separately, up to the sixth sub-band. The homomorphic enhancement is applied on the IR image in the log domain by decomposing the image into illumination and reflectance components. The illumination is attenuated, while the reflectance is magnified. Applying this method on each sub-band gives more details in the IR image. The performance quality metrics for the suggested approach are entropy, average gradient, contrast, and Sobel edge magnitude. Simulation results reveal the success of the proposed approach in enhancing the quality of IR images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Ashiba HI, Awadallah KH, El-Halfawy SM, Abd El-Samie FE (2008) Homomorphic enhancement of infrared images using the additive wavelet transform. Progress In Electromagnetics Research C 1:123–130

    Article  Google Scholar 

  2. Ashiba HI, Mansour HM, Ahmed HM, El-Kordy MF, Dessouky MI, El-Samie FEA (2018) Enhancement of infrared images based on efficient histogram processing. Wirel Pers Commun 99:619–636. https://doi.org/10.1007/s11277-017-4958-9

    Article  Google Scholar 

  3. Chan AL (2010) “A robust target tracking algorithm for FLIR imagery”, proc. SPIE Automatic Target Recognition May 7696:1–11

    Google Scholar 

  4. Ehsaeyan E (2016) An Improvement of Steerable Pyramid Denoising Method, Iranian Journal of Electrical & Electronic Engineering, Vol. 12, No. 1

  5. Gonzalez R, Woods R (2008) Digital Image Processing”, 3th Ed. Pearson Prentice Hall

  6. Gonzalez R, Woods R, Eddins S (2004) Digital Image Processing Using MATLAB”, Prentice Hall

  7. Jae S, LIM (1990) Two-Dimensional Signal and Image Processing, Prentice Hall Inc.

  8. Marshall S, Sicuranza GL (2006) Advances in Nonlinear Signal and Image Processing, Hindawi Publishing Corporation

  9. Petrou M, Bosdogianni P (1999) Image processing fundamentals

    Book  Google Scholar 

  10. Qi W, Han J, Zhang Y, Bai L-f (2016) Infrared image enhancement using cellular automata. Infrared Phys Technol 76:684–690

    Article  Google Scholar 

  11. Qi Y, He R, Lin H (2016) Novel infrared image enhancement technology based on the frequency compensation approach. Infrared Phys Technol 03:021. https://doi.org/10.1016/j.infrared.2016

    Article  Google Scholar 

  12. Song Q, Wang Y, Bai K (2016) High dynamic range infrared images detail enhancement based on local edge preserving filter. Infrared Phys Technol. https://doi.org/10.1016/j.infrared.2016.06.023

    Article  Google Scholar 

  13. Vincent OR, Folorunso O (2009) A Descriptive Algorithm for Sobel Image Edge Detection, Proceedings of Informing Science & IT Education Conference

  14. Yang Z, Hoseinzadeh M, Andrews A, Mayers C, Evans DT (2017) Rory Thomas Bolt, Janki Bhimani, Ningfang Mi, and Steven Swanson, “AutoTiering: Automatic Data Placement Manager In Multi-tier All-flash Datacenter”, 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC)

  15. Zhang X, Zhao N, Wang B, Tian Z, Dai Y, Ning P, Chen D (2016) Structure-inherent near-infrared fluorescent probe mediates apoptosis imaging and targeted drug delivery in VIVO. Dyes Pigments. https://doi.org/10.1016/j.dyepig.2016.11.022

    Article  Google Scholar 

  16. Zhu P, Ma X, Huang Z (2017) Fusion of infrared-visible images using improved multi-scale top-hat transform and suitable fusion rules. Infrared Phys Technol. https://doi.org/10.1016/j.infrared.2017.01.013

    Article  Google Scholar 

  17. Zhuqing J (2011) Study of Multi-Source Image Fusion Method in Transform Domain”, Ph.D. Thesis, Jiangnan University, Wuxi

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to H. I. Ashiba.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ashiba, H.I., Mansour, H.M., Ahmed, H.M. et al. Enhancement of IR images using histogram processing and the Undecimated additive wavelet transform. Multimed Tools Appl 78, 11277–11290 (2019). https://doi.org/10.1007/s11042-018-6545-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6545-9

Keywords

Navigation