Skip to content
Research Article Open access CC BY 4.0

An Effective Algorithm for Denoising Salt-And-Pepper Noise in Real-Time

Obed Appiah, James Benjamin Hayfron-Acquah, Michael Asante

Asian Journal of Research in Computer Science · pp. 14–27 · Published 29 Jun 2020

10.9734/ajrcos/2020/v6i130149

Abstract

For computer vision systems to effectively perform diagnoses, identification, tracking, monitoring and surveillance, image data must be devoid of noise. Various types of noises such as Salt-and-pepper or Impulse, Gaussian, Shot, Quantization, Anisotropic, and Periodic noises corrupts images making it difficult to extract relevant information from them. This has led to a lot of proposed algorithms to help fix the problem. Among the proposed algorithms, the median filter has been successful in handling salt-and-pepper noise and preserving edges in images. However, its moderate to high running time and poor performance when images are corrupted with high densities of noise, has led to various proposed modifications of the median filter. The challenge observed with all these modifications is the trade-off between efficient running time and quality of denoised images. This paper proposes an algorithm that delivers quality denoised images in low running time. Two state-of-the-art algorithms are combined into one and a technique called Mid-Value-Decision-Median introduced into the proposed algorithm to deliver high quality denoised images in real-time. The proposed algorithm, High-Performance Modified Decision Based Median Filter (HPMDBMF) runs about 200 times faster than the state-of-the-art Modified Decision Based Median Filter (MDBMF) and still generate equivalent output.

Image denoising median filter decision based median filters real-time image processing real-time computer vision

References (19)

  1. 1 Exploratory Data Analysis [DOI]
  2. 2 Robust Statistics: The Approach Based on Influence Functions [DOI]
  3. 3 A fast two-dimensional median filtering algorithm [DOI]
  4. 4 A New Directional Weighted Median Filter for Removal of Random-Valued Impulse Noise [DOI]
  5. 5 Order statistics in digital image processing [DOI]
  6. 6 Image denoising review: From classical to state-of-the-art approaches [DOI]
  7. 7 Robust Parameter Estimation in Computer Vision [DOI]
  8. 8 Fast median and bilateral filtering [DOI]
  9. 9 Median Filtering in Constant Time [DOI]
  10. 10 An Improved Median Filtering Algorithm for Image Noise Reduction [DOI]
  11. 11 100+ Times Faster Weighted Median Filter (WMF) [DOI]
  12. 12 An Alternate Algorithm for (3x3) Median Filtering of Digital Images [DOI]
  13. 13 A Separable Median Filter for Image Noise Smoothing [DOI]
  14. 14 Recursive weighted median filters admitting negative weights and their optimization [DOI]
  15. 15 Iterative median filtering for restoration of images with impulsive noise [DOI]
  16. 16 Modified decision based median filter for impulse noise removal [DOI]
  17. 17 Sort Optimization Algorithm of Median Filtering Based on FPGA [DOI]
  18. 18 MEDIAN FINDING ON A 3 × 3 GRID [DOI]
  19. 19 Computer vision: algorithms and applications [DOI]

Cited by 2

DTMF: Decision-based Trimmed Multimode approach Filter for denoising MRI images

B. Nisha, M. Victor Jose · Soft Computing · 2023

Showing 1 of 2 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

19

Page views

0

PDF downloads

7

Outbound clicks

2

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.