Skip to main content

Advertisement

SpringerLink
Log in

DE-IE: differential evolution for color image enhancement

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

Color images are not ready to provide a desired value of information because of illumination or some other conditions like settings of the captured instrument. So for improving the quality of color images and making them a good source of information an improvement of quality is desired sometimes. To improve the quality of an existing image or extract some features from a degraded image; image enhancement techniques are used. Many conventional algorithms are available for color image enhancement; some of them are based on linear gain adjustments. These algorithms will provide a limited improvement in an image. For making an overall improvement in an image many algorithms are advised based on genetic algorithm and particle swarm optimization. It is very well known that differential evolution is a very robust and simple algorithm for optimization. 1D histogram technique of image enhancement takes information about the pixel value and manipulates it to a required output value according the problem nature. Some relevant information of the pixel is not considered in 1D histogram technique; 2D histogram will be design considering all the relevant information around the pixel and manipulate it to an output pixel value according this information. Each pixel will behave like a member of population for differential evolution and manipulated on the basis of best value. Results show a significant and considerable change in output image. In this paper a new algorithm with differential evolution is proposed.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Arici T, Dikbas S, Altunbasak Y (2009) A histogram modification framework and its application and its application for image contrast enhancement. IEEE Trans on Image Process 18(9):1921–1935

  • Aslantas V, Tunckanat M (2007) Differential evolution algorithm for segmentation of wound images. In: Proceedings of IEEE international symposium on intelligent signal processing-2007, pp 1–5

  • Astola J, Haavisto P, Neuvo Y (1990) Vector median filters. In: Proceedings of the IEEE, pp 678–689

  • Celik T (2012) Two-dimensional histogram equalization and contrast enhancement. Pattern Recogn 3810–3824

  • Chander A, Chatterjee A, Siarry A (2011) A new social and momentum component adaptive PSO algorithm for image segmentation. Expert Syst Appl 38:4998–5004

    Article  Google Scholar 

  • Chen H, Leou J (2012) Saliency-directed color image interpolation using artificial neural network and particle swarm optimization. J Vis Commun Image R 23:343–358

    Article  Google Scholar 

  • Chen SY, Li YF, Zhang J (2008) Vision processing for realtime 3-d data acquisition based on coded structured light. IEEE Trans Image Process 17(2):167–176

    Article  MathSciNet  Google Scholar 

  • Chiu YS, Cheng FC, Huang SC (2011) Efficient contrast enhancement using adaptive gamma correction and cumulative intensity distribution. In: Proceedings of the IEEE international conference on systems, man, and cybernetics (IEEE SMC 2011), pp 2946–2950

  • Chung K, Yang W, Yan W (2008) Efficient edge-preserving algorithm for color contrast enhancement with application to color image segmentation. J Vis Commun Image R 19:299–310

    Article  Google Scholar 

  • Coelho LDS, Sauer JG, Rudek M (2009) Differential evolution optimization combined with chaotic sequences for image contrast enhancement. Chaos, Solitons Fractals 42:522–529

    Article  Google Scholar 

  • Cohen-Or D, Sorkine O, Gal R, Leyvand T, Xu YQ (2006) Color harmonization. In: Proceedings of the 2006 ACM SIGGRAPH, pp 624–630

  • Fan SS, Lin Y (2007) A multi-level thresholding approach using a hybrid optimal estimation algorithm. Pattern Recogn Lett 28:662–669

    Article  Google Scholar 

  • Feng D, Wenkang S, Liangzhou S, Yong D, Zhenfu Z (2005) Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO). Pattern Recogn Lett 26:597–603

    Article  Google Scholar 

  • Fernández-Caballero A, Castillo JC, Serrano-Cuerda J, Maldonado-Bascón S (2011) Real-time human segmentation in infrared videos. Expert Syst Appl 38:2577–2584

    Article  Google Scholar 

  • Ghamisi P, Couceiro MS, Benediktsson JA, Ferreira NMF (2012) An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst Appl 39:12407–12417

    Article  Google Scholar 

  • Gonzalez RC, Woods RE (2006) Digital Image Processing, 3rd edn. Prentice-Hall, Inc., Upper saddle river, NJ, USA

  • Hammouche K, Diaf M, Siarry P (2008) A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Comput Vis Image Underst 109:163–175

    Article  Google Scholar 

  • Hashemi S, Kiani S, Noroozi N, Ebrahimi MM (2010) An image contrast enhancement method based on genetic algorithm. Pattern Recogn Lett 31:1816–1824

    Article  Google Scholar 

  • Horng M (2010) Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization. Expert Syst Appl 37:4580–4592

    Article  Google Scholar 

  • Hoseini P, Shayesteh MG (2013) Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing. Digit Signal Proc 23:879–893

    Article  MathSciNet  Google Scholar 

  • Huang SC, Yeh CH (2013) Image contrast enhancement for preserving mean brightness without losing image features. Eng Appl Artif Intell 26:1487–1492

    Article  Google Scholar 

  • Huang K, Wang Q, Wu Z (2006) Natural color image enhancement and evaluation algorithm based on human visual system. Comput Vis Image Underst 103:52–63

    Article  Google Scholar 

  • Jiang J, Yao B, Wason AM (2007) A genetic algorithm design for microcalcification detection and classification in digital mammograms. Comput Med Imaging Graph 31:49–61

    Article  Google Scholar 

  • Kumar S, Pant M, Ray AK (2011) Differential evolution embedded Otsu’s method for optimized image thresholding. In: International conference world congress on information and communication technologies WICT-2011, pp 325–329

  • Kumar S, Kumar P, Sharma TK, Pant M (2013) Bi-level thresholding using PSO, artificial bee colony and MRLDE embedded with Otsu method. Memetic Comput. doi:10.1007/s12293-013-0123-5

  • Kwok NM, Shi HY, Ha QP, Fang G, Chen SY, Jia X (2013) Simultaneous image color correction and enhancement using particle swarm optimization. Eng Appl Artif Intell

  • Lan J, Zeng Y (2013) Multi-threshold image segmentation using maximum fuzzy entropy based on a new 2D histogram. Optik 124:3756–3760

    Article  Google Scholar 

  • Lee C, Leou J, Hsiao H (2012) Saliency-directed color image segmentation using modified particle swarm optimization. Sig Process 92:1–18

    Article  Google Scholar 

  • Li L, Li D (2008) Fuzzy entropy image segmentation based on particle swarm optimization. Prog Nat Sci 18:1167–1171

    Article  Google Scholar 

  • Li Z, Zhang D, Xu Y, Liu C (2011) Modified local entropy-based transition region extraction and thresholding. Appl Soft Comput 11:5630–5638

    Article  Google Scholar 

  • Masra SMW, Pang PK, Muhammad MS, Kipli K (2012) Application of particle swarm optimization in histogram equalization for image enhancement. In: IEEE colloquium on humanities, science and engineering research (CHUSER 2012), pp 294–299

  • Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmention algorithms and measuring ecological statistics. In: Proceedings of 8th International Conference on Computer, vol 2, pp 416–423

  • Nakib A, Oulhadj H, Siarry P (2007) Image histogram thresholding based on multi-objective optimization. Sig Process 87:2516–2534

    Article  MATH  Google Scholar 

  • Osuna-Enciso V, Cuevas E, Sossa H (2013) A comparison of nature inspired algorithms for multi-threshold image segmentation. Expert Syst Appl 40:1213–1219

    Article  Google Scholar 

  • Sathya PD, Kayalvizhi R (2011) Modified bacterial foraging algorithm based multilevel thresholding for image segmentation. Eng Appl Artif Intell 24:595–615

    Article  Google Scholar 

  • Shanmugavadivu P, Balasubramanian K (2013) Particle swarm optimized multi-objective histogram equalization for image enhancement. Opt Laser Technol

  • Shyu M, Leou J (1998) A genetic algorithm approach to color image enhancement. Pattern Recogn 871–880

  • Starck JL, Murtagh F, Candes EJ, Donoho DL (2000) Gray and color image contrast enhancement by the curve let transform. IEEE Trans Image Process 12:706–717

    Article  MATH  Google Scholar 

  • Tao W, Tian J, Liu J (2003) Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm. Pattern Recogn Lett 24:3069–3078

    Article  Google Scholar 

  • Tao W, Jin H, Liu L (2007) Object segmentation using ant colony optimization algorithm and fuzzy entropy. Pattern Recogn Lett 28:788–796

    Article  Google Scholar 

  • Tsai H, Jhuang Y, Lai Y (2012) An SVD-based image watermarking in wavelet domain using SVR and PSO. Appl Soft Comput 12:2442–2453

    Article  Google Scholar 

  • Vahedi E, Zoroofi RA, Shiva M (2012) Toward a new wavelet-based watermarking approach for color images using bio-inspired optimization principles. Digit Signal Proc 22:153–162

    Article  Google Scholar 

  • Verma OP, Kumar P, Hanmandlu M, Chhabra S (2012) High dynamic range optimal fuzzy color image enhancement using artificial ant colony system. Appl Soft Comput 12:394–404

    Article  Google Scholar 

  • Wang C, Peng J, Ye Z (2008) Flattest histogram specification with accurate brightness preservation. IET Image Process 2(5):249–262

  • Zahara E, Fan SS, Tsai D (2005) Optimal multi-thresholding using a hybrid optimization approach. Pattern Recogn Lett 26:1082–1095

    Article  Google Scholar 

  • Zaharescu E, Zamfir M, Vertan C (2003) Color morphology-like operators based on color geometric shape characteristic. In: Proceedings of international symposium on signal circuit and systems, Iasi, Romania, July 2003, pp 145–148

  • Zhang C, Wang X, Duanmu C (2010) Adaptive typhoon cloud image enhancement using genetic algorithm and non-linear gain operation in undecimated wavelet domain. Eng Appl Artif Intell 23:61–73

    Article  Google Scholar 

  • Zhong S, Jiang X, Wei J, Wei Z (2013) Image enhancement based on wavelet transformation and pseudo-color coding with phase-modulated image density processing. Infrared Phys Technol 58:56–63

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Sushil Kumar, Millie Pant & Amiya Kumar Ray

Authors
  1. Sushil Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Millie Pant
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amiya Kumar Ray
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sushil Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, S., Pant, M. & Ray, A.K. DE-IE: differential evolution for color image enhancement. Int J Syst Assur Eng Manag 9, 577–588 (2018). https://doi.org/10.1007/s13198-014-0278-6

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-014-0278-6

Keywords

Search

Navigation