A Modified Artificial Bee Colony Algorithm for Image Denoising Using Parametric Wavelet Thresholding Method

Abstract

In the wavelet transform domain, the wavelet thresholding denoising is an effective noise reduction method for noisy images. The key issues of image thresholding denoising are the choice of threshold value and construction of thresholding function. To overcome the shortcomings in the classical wavelet thresholding methods such as fixed threshold value and inflexible thresholding function, a modified artificial bee colony (MABC) algorithm-based parametric wavelet thresholding approach is utilized. A construction scheme of parametric wavelet thresholding function is firstly put forward. And a new tangent function-based thresholding is built based on the construction method. The threshold value and shape tuning parameter of the proposed thresholding are initialized as the possible solutions of the MABC algorithm, and the corresponding objective function is minimized. Finally, the MABC-based approach is applied to process two types of images with different degrees of degradation. It is also compared with classical thresholding and other optimization algorithm-based methods in terms of different criteria. Comparison results demonstrate that the proposed MABC-based thresholding approach achieves better enhancement in terms of denoising capability.