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

نویسندگان

چکیده

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

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

An Improved BP Neural Network Algorithm Based on Factor Analysis

Back-Propagation (BP) neural network, as one of the most mature and most widespread algorithms, has the ability of large scale computing and has unique advantages when dealing with nonlinear high dimensional data. But when we manipulate high dimensional data with BP neural network, many feature variables provide enough information, but too many network inputs go against designing of the hidden-...

متن کامل

Face Recognition Algorithm Based on Improved BP Neural Network

Face recognition has received wide concern as a hot direction in recognition models. Due to a strong self-adaptive and mapping ability, traditional BP algorithm occupies certain advantages in face recognition, but is has the shortcomings of fast convergence speed and being easy to fall into local optimum in itself. In this paper, an improved BP neural network is proposed aiming at the deficienc...

متن کامل

Telephone Traffic Forecasting Based on Grey Neural Network Optimized by Improved Particle Swarm Optimization Algorithm

To solve the problem that the parameters in grey neural network (GNN) are difficult to determine, the improved Particle Swarm Optimization (IPSO) algorithm is employed to search the optimums by the introduction of a threshold of velocity. When the particle velocity is less than the threshold, an accelerated momentum is applied on the particle to reinitialize the particle velocity and position. ...

متن کامل

Research on Fault Diagnosis Based on BP Neural Network Optimized by Chaos Ant Colony Algorithm

In view of shortcomings of BP neural network, which is slow to converge and tends to trap in local optimum when applied in fault diagnosis, an approach for fault diagnosis based on BP neural network optimized by chaos ant colony algorithm is proposed. Mathematical model of chaos ant colony algorithm is created. Real-coded method is adopted and the weights and thresholds of BP neural network are...

متن کامل

Research of BP Neural Network based on Improved Particle Swarm Optimization Algorithm

The paper proposes an approach to optimize the connection weights and network structure of BP neural network (BPNN) which based on improved particle swarm optimization (PSO) algorithm. For each network structure, the algorithm generates a series of particles which consist of connection weights and threshold values, and selects the best network structure according to the improved PSO algorithm. ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Eurasip Journal on Wireless Communications and Networking

سال: 2021

ISSN: ['1687-1499', '1687-1472']

DOI: https://doi.org/10.1186/s13638-021-02013-2