Home            Contact us            FAQs
    
      Journal Home      |      Aim & Scope     |     Author(s) Information      |      Editorial Board      |      MSP Download Statistics

     Research Journal of Applied Sciences, Engineering and Technology


Optimization of Memory Management in Image Processing using Pipelining Technique

1P.S. Ramesh and 2S. Letitia
1Departmnet of Computer Science and Engineering, Arunai Engineering College, Tiruvannamalai, 606603
2Department of Electronics and Communication Engineering/Project Associate-TEQIP, Directorate of Technical Education, Guindy, Chennai, 600025, India
Research Journal of Applied Sciences, Engineering and Technology  2015  4:239-244
http://dx.doi.org/10.19026/rjaset.9.1400  |  © The Author(s) 2015
Received: ‎July ‎14, ‎2014  |  Accepted: September ‎25, ‎2014  |  Published: February 05, 2015

Abstract

The quality of the image is mainly based on the various phenomena which generally consume lots of memory that needs to be resolved addressed. The handling of the memory is mainly affected due to disorderly arranged pixels in an image. This may lead to salt and pepper noise which will affect the quality of the image. The aim of this study is to remove the salt and pepper noise which is most crucial in image processing fields. In this study, we proposed a technique which combines adaptive mean filtering technique and wavelet transform technique based on pipeline processing to remove intensity spikes from the image and then both Otsu’s and Clahe algorithms are used to enhance the image. The implemented framework produces good results and proves against salt and pepper noise using PSNR algorithm.

Keywords:

Adaptive filter , image processing , pipeline, wavelet transform,


References

  1. Aiswarya, K., V. Jayaraj and D. Ebenezer, 2010. A new and efficient algorithm for the removal of high density salt and pepper noise in images and videos. Proceeding of 2nd International Conference on Computer Modeling and Simulation, pp: 409-413.
    CrossRef    
  2. Bellas, N. and A. Yanof, 2006. An image processing pipeline with digital compensation of low cost optics for mobile telephony. Proceeding of IEEE International Conference on Multimedia and Expo, pp: 1249-1252.
    CrossRef    
  3. Bovik, A.C., 2005. Handbook of Image and Video Processing. Elsevier Academic Press, Burlington, MA.
  4. Buades, A., B. Coll and J.M. Morel, 2005. A non-local algorithm for image denoising. Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR, 2005), pp: 20-25.
    CrossRef    
  5. Duan, D., Q. Mo, Y. Wan and Z. Han, 2010. A detail preserving filter for impulse noise removal. Proceeding of IEEE International Conference on Computer Application and System Modeling, pp: 265-268.
    PMid:21091387    
  6. Gonzalez, R.C. and R. E. Woods, 2008. Digital Image Processing. Prentice Hall, Upper Saddle River, NJ.
  7. Jayaraj, V. and D. Ebenezer, 2010. A new switching-based median filtering scheme and algorithm for removal of high-density salt and pepper noise in image. EURASIP J. Adv. Sig. Pr., 2010: 690218.
    CrossRef    
  8. Kao, W.C. and Y.J. Chen, 2005. Multistage bilateral noise filtering and edge detection for color image enhancement. IEEE T. Consum. Electr., 51(4): 1346-1350.
    CrossRef    
  9. Kao, W.C., S.H. Wang, L.Y. Chen and S.Y. Lin, 2006. Design considerations of color image processing pipeline for digital cameras. IEEE T. Consum. Electr., 52(4): 1144-1152.
    CrossRef    
  10. Luo, W., 2006. An efficient detail-preserving approach for removing impulse noise in images. IEEE Signal Proc. Let., 13(7): 413-416.
    CrossRef    
  11. Maatta, J.M., J. Vanne, T.D. Hamalainen and J. Nikkanen, 2011. Generic software framework for a line-buffer-based image processing pipeline. IEEE T. Consum. Electr., 57(3): 1442-1449.
    CrossRef    
  12. Ng, P.E. and K.K. Ma, 2006. A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE T. Image Process., 15(6): 1506-1516.
    CrossRef    PMid:16764275    
  13. Nikkanen, J., T. Gerasimow and L. Kong, 2008. Subjective effects of white-balancing errors in digital photography. Opt. Eng., 47(11): 113201-1-113201-15.
  14. SMIA 1.0: Introduction and Overview, 2004. Standard Mobile Imaging Architecture (SMIA).
  15. Sreenivasan, K.S. and D. Ebenezer, 2007. A new fast and efficient decision-based algorithm for removal of high-density impulse noises. IEEE Signal Lett., 14(3): 189-192.
    CrossRef    
  16. Srinivas, K.S. and K.N.L. Kalyani, 2011. Transform coefficient histogram and edge preserving image enhancement using contrast entropy. Int. J. Comput. Technol. Appl., 2(6): 1854-1858.
  17. Zhang, H. and L. Lucchese, 2004. A fast tone reproduction algorithm for high dynamic range image display. Proceeding of 6th IEEE Workshop on Multimedia Signal Processing, pp: 275-278.
    CrossRef    
  18. Zhang, S. and M.A. Karim, 2002. A new impulse detector for switching median filters. IEEE Signal Proc. Let., 9(11): 360-363.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Information
   Sales & Services
Home   |  Contact us   |  About us   |  Privacy Policy
Copyright © 2024. MAXWELL Scientific Publication Corp., All rights reserved