A Hybrid Cellular Automata - Patch Based Local Principal Component Analysis Techniques for Improving Image De-Noising

Authors

  • Suresh. A Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
  • Ranjithkumar. S Assistant Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
  • Nithya. N S Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
  • S. Vinoth Kumar Associate Professor, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India
  • S. Girirajan Assistant Professor, Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India

Keywords:

Cellular Automata; Image denoising; Dimensionality Reduction; Principal Component Analysis; Speckle Suppression Index SSI; Speckle Suppression and Mean Preservation Index SMPI

Abstract

The primary motivation behind this article lies in addressing the growing demand for image processing applications within critical environments where the presence of noise can significantly degrade the quality of the outputs. Existing methods primarily consider the noisy version of the image for denoising, neglecting crucial properties such as the dimension of image pixels. This oversight can lead to inaccurate estimates of image values. In the case of Patch-based Local Principal Component Analysis (PL-PCA) filters, the choice of similarity weights and the reduction of sample sizes are pivotal factors. Increasing the sample size can complicate image handling. Regrettably, existing filtering techniques focus solely on noise removal without taking into account the size of input samples. This article aims to resolve these challenges by introducing the H-PL-PCA (Hybrid Patch-based Principal Component Analysis) approach for efficient image noise filtering. The proposed method addresses issues related to dimensionality reduction and the selection of neighboring cells, ensuring that the end results are obtained through the comprehensive analysis of various parameters for effective denoising.

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Published

27.12.2023

How to Cite

A, S., S, R. ., N S, N. ., Kumar, S. V. ., & Girirajan, S. . (2023). A Hybrid Cellular Automata - Patch Based Local Principal Component Analysis Techniques for Improving Image De-Noising. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 61–71. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4204

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

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