Some studies on improved image denoising technique using slime mould algorithm

Abstract

This Thesis presents an improved denoising method based on the order of cascaded filters. The amalgamations of different filters are obtained through the performance of Slime Mould Algorithm (SMA) and cascading four filters out of twelve filters. The proposed algorithm has several new features which provide enhanced denoised images. SMA has a new and individual mathematical model that uses adaptive measures to stimulate the affair of fabricating positive and negative feedback of the propagation wave of slime mould, and the entire model is based on a bio-oscillator to form the optical path for collecting food with excellent exploitation propensity and exploratory ability. Most of the existing denoising techniques are suitable for either Salt & Pepper, Gaussian, or Speckle noise but our proposed algorithm is helpful in removing all three noises. The denoised images are far better in terms of both visual and quantitative analysis. The proposed algorithm also provides a significant improvement compared to other algorithms.