Application of Douglass-Gunn ADI Scheme on Diffusion Model with Different Noise Level for Image Denoising

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Noise level is the amount of noise that corrupted the clear image in order to test on the filtering method of an algorithm proposed for image denoising. Most of the existing filtering techniques are able to remove noise but unable to preserve the image detail well and hence causing the blurring effect. Due to that, the objectives of this paper are to propose and implement Douglas-Gunn Alternating Direction Implicit (DG-ADI) on Anisotropic Diffusion (AD) model. Then, measure the performance of the proposed scheme with different level of noise. PDE based model is applied with the unconditional stable of DG-ADI scheme to remove the noise that corrupted the images. The AD model is used for preserving the image structures and edges. In this paper, a set of grayscale images from standard database is being filtered with three different noise levels in order to measure the performance of the proposed schemes. The performance of the proposed scheme is measured using the Mean Structural Similarity Index (MSSIM), Peak Signal to Noise Ratio (PSNR), Universal Image Quality Index (UIQI) and processing time. The implementation of the algorithm is completed using MATLAB R2013a. Experimental results show that the DG-ADI scheme able to remove noise with different noise level. The used of DG-ADI scheme in solving the AD model can remove the noise well without destroy the structure of image with appropriate parameters setting in grayscale image.

     

     


  • Keywords


    Anisotropic Diffusion; Douglass-Gunn ADI; Image Denoising; MATLAB; Noise.

  • References


      [1] Chuah JH, Khaw HY, Soon FC & Chow C (2017), Detection of Gaussian Noise and Its Level using Deep Convolutional Neural Network. Proceedings of the IEEE Region 10 Conference, 2447–2450.

      [2] Farooque MA & Rohankar JS (2013), Survey on Various Noises and Techniques for Denoising the Color Image. International Journal of Application or Innovation in Engineering and Management 2(11), 217–221.

      [3] Guo Z, Sun J, Zhang D & Wu B (2012), Adaptive Perona-Malik Model based on the Variable Exponent for Image Denoising. IEEE Transactions on Image Processing 21(3), 958–967.

      [4] Halim SA, Razak RA, Ibrahim A & Manurung YH (2014), Perona Malik Anisotropic Diffusion Model using Peaceman Rachford Scheme on Digital Radiographic Image. AIP Conference Proceedings 1602(1), 208–214.

      [5] Isogawa K, Ida T, Shiodera T & Takeguchi T (2018), Deep Shrinkage Convolutional Neural Network for Adaptive Noise Reduction. IEEE Signal Processing Letters 25(2), 224–228.

      [6] Kamalaveni V, Rajalakshmi RA & Narayanankutty KA (2015), Image Denoising Using Variations of Perona-Malik Model with Different Edge Stopping Functions. Procedia Computer Science 58, 673–682.

      [7] Kaur S (2015), Noise Types and Various Removal Techniques. International Journal of Advanced Research in Electronics and Communication Engineering 4(2), 226-230.

      [8] Li S, Zhang D & Wu B (2011). A New Anisotropic Diffusion Model for Image Denoising. Proceedings of the International Conference on Multimedia Technology, pp. 5707–5710.

      [9] McDonough J (2007), Lectures on Computational Numerical Analysis of Partial Differential Equations, Lecture Notes. http://pdf-release.net/external/1997378/pdf-release-dot-net-me690-lctr-nts.pdf.

      [10] Mihcak MK (1999), Low-complexity Image Denoising based on Statistical Modeling of Wavelet Coefficients. IEEE Signal Processing Letters 6(12), 300–303.

      [11] Ostojic V, Starcevic D & Petrovic V (2016), Recursive Anisotropic Diffusion Denoising. Electronics Letters 2(17), 1449-1451.

      [12] Patidar P, Gupya M, Sirvastava S & Nagawat AK (2010), Image De-noising by Various Filters for Different Noise. International Journal of Computer Applications 9(4), 45–50.

      [13] Perona P & Malik J (1990), Scale-space and Edge Detection using Anisotropic Diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629–639.

      [14] Tsiotsios C & Petrou M (2013), On the Choice of the Parameters for Anisotropic Diffusion in Image Processing. Pattern Recognition 46(5), 1369–1381.

      [15] Weickert J (1998), Anisotropic Diffusion in Image Processing. B. G. Teubner (Stuttgart), pp.15–25.

      [16] Witkin AP (1983), Scale-space Filtering. Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1019–1022.

      [17] Yu J, Zhai R & Yie J (2017), Image Denoising Method Based on PM Model with Transforming Edge Stopping Function. Proceedings of the 2017 IEEE 2nd Inernational Information Technology, Networking, Electronic and Automation Control Conference, pp. 438–442.


 

View

Download

Article ID: 23473
 
DOI: 10.14419/ijet.v7i4.33.23473




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.