Image Denoising Based on Artificial Bee Colony and BP Neural Network
Abstract: Image is often
subject to noise pollution during the process of collection, acquisition and transmission,
noise is a major factor affecting the image quality, which has greatly impeded
people fromextracting information from the image. The purpose of image
denoising is to restore the original imagewithout noise from the noise image,
and at the same time maintain the detailed information of the image as much as
possible. This paper, by combining artificial bee colony algorithm and BP
neural network, proposes the image denoising method based on artificial bee
colony and BP neural network (ABC-BPNN), ABC-BPNN adopts the “double
circulation” structure during the training process, after specifying the expected
convergence speed and precision, it can adjust the rules according to the
structure, automatically adjusts the number of neurons, while the weight of the
neurons and relevant parameters are determined through bee colony optimization.
The simulation result shows that the algorithm proposed in this paper can maintain
the image edges and other important features while removing noise, so as to
obtain better denoising effect.
Author: Junping Wang, Dapeng
Zhang
Journal Code: jptkomputergg150086
