Total views : 165

Encrypted Image Denoising using Adaptive Weighted Median Filter


  • Amity School of Engineering and Technology, Amity University, Lucknow – 226010, Uttar Pradesh, India
  • Electronics and Communication Department, BIET, Jhansi – 284001, Uttar Pradesh, India


Background /Objectives: A novel approach for image encryption using chaotic functions and for denoising encrypted image using adaptive weighted median filter is proposed. The proposed method is implemented for encryption and quality improvement of biometric images. Methods/Statistical Analysis: The proposed techniques are simulated using MATLAB platform. Qualitative and quantitative statistical analyses have been done using performance parameters such as Peak Signal to Noise Ratio, Mean Square Error, and Structural Similarity Index. Denoising of biometric images is computed based on these performance parameters. Findings: Experimental computation of proposed algorithm shows how a high peak signal to noise ratio, low mean square error and comparable structural similarity index infers the effectiveness of chaotic algorithm for image encryption. Further denoising procedure using adaptive weighted median filter is utilized for further enhancing and improving biometric image quality while suppressing noise present in the image.Application/Improvements: The proposed method for image encryption and denoising of biometric image provides a secure way for encryption and relatively effective image quality improvement as compared to existing models.


Adaptive Weighted Median Filter (AWMF), Encryption, Denoising, Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) , Structural Similarity Index (SSIM)

Full Text:

 |  (PDF views: 162)


  • Bellare M. Rogaway, Phillip. Introduction to Modern Cryptography. 2005.p.10.
  • Jain A, Hong L, Pankanti S. Biometric Identification.Communications of the ACM. 2000;43(2): 91–98. CrossRef.
  • Jain AK, Flynn, Ross A. Introduction to Biometrics.Handbook of Biometrics. Springer. 2008.p. 1–22.
  • Weaver AC. Biometric Authentication. Computers (IEEE).2006 Feb, 39(2), pp. 96–97. CrossRef.
  • Luo W. An efficient detail-preserving approach for removing impulse noise in images. IEEE Signal Processing Letters.2006;13(7): 413–16. CrossRef.
  • Shanmugavadivu P, Jeevaraj E. Adaptive PDE-Based Median Filter for the Restoration of High- Density Impulse Noise Corrupted Images. International Journal of Advanced Information Technology (IJAIT). 2011;1(6):1-9. CrossRef.
  • Wang, Zhang D. Progressive switching median filter for the removal of impulse noise from highly corrupted images.IEEE Trans Circuits System II, Analog Digit Signal Process. 1999;46(1):78–80. CrossRef.
  • Hosseini H, Marvasti F. Fast restoration of natural images corrupted by high-density impulse Noise. EURASIP Journal based on Image and Video Processing. 2013,p.15.
  • Zeghid M, Machhout M, Khriji L, Baganne A, Tourki R. A Modified AES Based Algorithm for Image Encryption.
  • World Academy of Science, Engineering and Technology.2004.p.27–32.
  • Zhang L, Liao X., Wang X. An image encryption approach based on chaotic maps. Chaos, Solitons& Fractals.2005 August;24(1):759–65. CrossRef.
  • Chen G, Mao Y, Chui C. A symmetric encryption scheme based on 3D chaotic cat map. Chaos, Solitons& Fractals.2004;21(2):749–61. CrossRef.
  • Kocarev L, Jakimoski G, Stojanovski T, Parlitz U. From chaotic maps to encryption schemes. Proceedings IEEE International Symposium of Circuits System. 1998.p.514– 17. CrossRef.
  • Jakimoski G, Kocarev L. Chaos and cryptography: Block encryption ciphers based on chaotic maps. IEEE Trans.Circuits Systems. 2001 Feb; 48(2):163–69. CrossRef.
  • Li S, Zheng X. Cryptanalysis of a chaotic image encryption method. Proceedings IEEE International Symposium of Circuits System. 2002.p.708–11. PMid:12133473
  • Mao Y, Chen G, Lian S. A novel fast image encryption scheme based on 3D chaotic baker maps. International Journal of Bifurcate Chaos. 2004;14(10):3613–624. CrossRef.
  • Maniccam SS, Bourbakis NG. Lossless image compression and encryption using SCAN. Pattern Recognition.
  • ;34(6):1229–245. CrossRef.
  • Gu G, Han G. An Enhanced Chaos Based Image Encryption Algorithm. IEEE Proceedings of the First International Conference on Innovative Computing, Information and Control. 2006;1: 492–95. CrossRef.
  • Zhou Y, Bao L, Chen CLP. A new 1D chaotic system for image encryption. Signal Processing Elsevier. 2014;97:172– 82. CrossRef.
  • Alsafasfeh A, Arfoa. Image Encryption Based on the General Approach for Multiple Chaotic Systems. Journal of Signal and Information Processing. 2011;2(3):238–44. CrossRef.
  • Uludag U, Pankanti S, Jain AK. Biometric cryptosystems: issues and challenges. Proceedings of the IEEE: Special Issue on Enabling Security Technologies for Digital Rights Management. 2004; 92(6): 948–59. CrossRef.
  • Yen JC, Guo JI. A new key-based design for image encryption and decryption. Proceedings IEEE Circuits System.2000.p. 49–52. PMid:10629012
  • Aoki H, Watanabe E, Nagata A, Kosugi Y. Rotation-Invariant Image Association for Endoscopic Positional Identification Using Complex-Valued Associative Memories. IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II. 2010.p. 369–76.
  • Banham, Mark R, Katsaggelos AK. Digital image restoration.Signal Processing Magazine IEEE. 1997;14(2):24–41.CrossRef.
  • Mandal JK, Sarkar A. A Modified Weighted Based Filter for Removal of Random Impulse Noise (MWB). IEEE Trans.2011;2(4): 1–10. CrossRef.
  • Jain AK. Fundamentals of digital image processing. PrenticeHall. 1989.p.1–10.
  • Boyat A, Joshi BK. Image Denoising using Wavelet Transform and Median Filtering. IEEE Nirma University International Conference on Engineering, Ahmedabad.2013.p. 1–8. CrossRef.
  • Kumar R, Srivastava SK. EHW Architecture for Design of Adaptive Median Filter for Noise Reduction European Journal of Scientific Research. 2009.p.273–79.
  • Simoncelli EP, Adelson E. Noise removal via Bayesian wavelet coring. Proceeding IEEE International Conference on Image Processing. 1996.p.279–382. CrossRef.
  • Moody A, Jing Li, Xiao, Feng J. A flexible and detail preserving adaptive filter for speckle noise suppression.
  • International Journal of Remote Sensing. 2003;12(2):2451– 465.
  • Qiu F, Berglund J, Jensen JR, Thakkar P, Ren D. Speckle noise reduction in SAR imagery using a local adaptive median filter. GI Science & Remote Sensing. 2004;41(3):244–66.CrossRef.
  • Sinha A, Singh KA. Technique for Image Encryption using Digital Signatures. Optics Communication. 2003.p. 229– 34. CrossRef.
  • Xiao HP, Zhang GJ. An Image Encryption Scheme Based On Chaotic Systems. IEEE Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian. 2006.p. 2707–711. CrossRef.
  • Pareek, NK, Patidar V, Sud KK. Image encryption using chaotic logistic map. 2006.p. 926–34.
  • Simoncelli EP, Adelson E. Noise removal via Bayesian wavelet coring. Proceeding IEEE International Conference on Image Processing. 1996.p.279–382. CrossRef.
  • Chipman HA, Kolaczyk ED, McCulloch RE. Adaptive Bayesian wavelet shrinkage. J.Amer. stst. Association. 1997; 92(440):1413–421. CrossRef.
  • Feras N, Jabar H, Yousif H, Nebras NH, Ramli AR. Image Enhancement Using Nonlinear Filtering Based Neural Network. Journal of Computing. 2011 May; 2(3): 413–20.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.