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

• Published in: EURASIP journal on wireless communications and networking Vol. 2021; no. 1; pp. 1 - 22
• Main Authors: Wang, Chunzhi, Li, Min, Wang, Ruoxi, Yu, Han, Wang, Shuping
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 26.06.2021; Springer Nature B.V; SpringerOpen
• Subjects: Adaptive weight coefficient; Algorithms; Back propagation networks; BP neural network; Communications Engineering; Convergence; Engineering; Image denoising; Image enhancement; Image segmentation; Information Systems Applications (incl.Internet); Model accuracy; Networks; Neural networks; Noise; Noise pollution; Noise reduction; Nonlinear convergence factor; Object recognition; Optimization; Optimization algorithms; Security & Privacy Solutions for Smart City; Signal to noise ratio; Signal,Image and Speech Processing; Weight; Whale optimization algorithm; Wiener filtering; BP neural network; Whale optimization algorithm; Nonlinear convergence factor; Adaptive weight coefficient; Image denoising
• ISSN: 1687-1499; 1687-1472; 1687-1499
• DOI: 10.1186/s13638-021-02013-2

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.