An image denoising method based on BP neural network optimized by improved whale optimization algorithm

Chunzhi Wang,Han Yu,Ruoxi Wang,Shuping Wang,Min Li

doi:10.1186/s13638-021-02013-2

Abstract

As an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

Highlights

Smart city refers to the use of new generation of information technology to connect and integrate the city’s systems and services, so as to improve the efficiency of resource utilization, optimize city services, achieve fine and dynamic management, and improve the quality of life of citizens [1]
1.1 Our contributions Based on the existing research results, this paper proposes an image denoising method based on back propagation (BP) neural network optimized by improved whale optimization algorithm
3.2.3 Steps of MSWOA‐BP method The specific steps to obtain the initial weights and thresholds of the BP neural network by improved whale optimization algorithm are as follows: Step 1 Determine the topology of BP neural network, and set the number of nodes in the input layer, hidden layer, and output layer of the neural network and other related parameters; Step 2 Initialize N, Max_iter, lb, ub and other parameters in whale optimization algorithm, and calculate the dimension dim according to the number of nodes in each layer of BP neural network set in step1

Summary

Introduction

Smart city refers to the use of new generation of information technology to connect and integrate the city’s systems and services, so as to improve the efficiency of resource utilization, optimize city services, achieve fine and dynamic management, and improve the quality of life of citizens [1]. 1.1 Our contributions Based on the existing research results, this paper proposes an image denoising method based on BP neural network optimized by improved whale optimization algorithm. In order to solve the problems of BP neural network in the process of image denoising that the convergence speed is slow and it is easy to fall into the local extreme value, great efforts have been made to improve it. The centroid mutation operator is introduced on the basis of traditional copula distribution estimation algorithm, which effectively overcomes the shortcoming that BP neural network is easy to fall into local optimum in image denoising, but the overall complexity of the model is high and the overall performance is poor. The existing research has improved the optimization effect of the standard WOA, the problems such as the balance between global exploration and local development ability, slow convergence speed and easy to fall into local optimization still need to be further studied [44,45,46]

Methodology

Results and discussion

F PSO Avg

Objective space

Conclusion