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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 4065306, 11 pages
https://doi.org/10.1155/2017/4065306
Research Article

A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection

1School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China
2Institute of Petroleum and Chemical Industry, Shenyang University of Technology, Shenyang, Liaoning 110870, China

Correspondence should be addressed to Gang Wang

Received 18 August 2016; Revised 29 January 2017; Accepted 2 March 2017; Published 5 April 2017

Academic Editor: Lotfi Senhadji

Copyright © 2017 Gang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Wavelet transform is an effective method for removal of noise from image. But traditional wavelet transform cannot improve the smooth effect and reserve image’s precise details simultaneously; even false Gibbs phenomenon can be produced. This paper proposes a new image denoising method based on adaptive multiscale morphological edge detection beyond the above limitation. Firstly, the noisy image is decomposed by using one wavelet base. Then, the image edge is detected by using the adaptive multiscale morphological edge detection based on the wavelet decomposition. On this basis, wavelet coefficients belonging to the edge position are dealt with with the improved wavelet domain wiener filtering, and the others are dealt with with the improved Bayesian threshold and the improved threshold function. Finally, wavelet coefficients are inversely processed to obtain the denoised image. Experimental results show that this method can effectively remove the image noise without blurring edges and highlight the characteristics of image edge at the same time. The validation results of the denoised images with higher peak signal to noise ratio (PSNR) and structural similarity (SSIM) demonstrate their robust capability for real applications in the future.