Image Denoising based on Back Propagation Neural Network and Wavelet Decomposition

نویسندگان

  • Yanliang Zhang
  • Bing Lu
چکیده

In this paper, an image denoising method based on back propagation neural network and wavelet decomposition is proposed. The wavelet decomposition coefficient of the image with noise is used as the training sample input, and the high frequency coefficient of the original image is used as the expected output to train the BP network. The obtained network can remove the noise in the image better. The experimental results are compared with the results of denoising using mean filtering, median filtering and Wiener filtering. The comparison results also show that the proposed method has some merits such as eliminating the noise of point and line regions.

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Neural Network Performance Analysis for Real Time Hand Gesture Tracking Based on Hu Moment and Hybrid Features

This paper presents a comparison study between the multilayer perceptron (MLP) and radial basis function (RBF) neural networks with supervised learning and back propagation algorithm to track hand gestures. Both networks have two output classes which are hand and face. Skin is detected by a regional based algorithm in the image, and then networks are applied on video sequences frame by frame in...

متن کامل

Image Classification by Combining Wavelet Transform and Neural Network

In this paper, we propose a method of classification of image by combining wavelet transform and neural network. Our main objective in this work is to achieve an optimal approach of classification by combining wavelet transform and neural network. The proposed scheme for successful classification is combination of a wavelet domain feature extractor and back propagation neural networks (BPNN) cl...

متن کامل

Comparative Analysis of Image Denoising Methods Based on Wavelet Transform and Threshold Functions

There are many unavoidable noise interferences in image acquisition and transmission. To make it better for subsequent processing, the noise in the image should be removed in advance. There are many kinds of image noises, mainly including salt and pepper noise and Gaussian noise. This paper focuses on the research of the Gaussian noise removal. It introduces many wavelet threshold denoising alg...

متن کامل

Classification of ECG signals using Hermite functions and MLP neural networks

Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. In the first module the stationary wavelet transform (SWF) is used for noise reduction of ...

متن کامل

Statistical Wavelet-based Image Denoising using Scale Mixture of Normal Distributions with Adaptive Parameter Estimation

Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wa...

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

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017