Next Article in Journal
Potentiality of Using Luojia1-01 Night-Time Light Imagery to Estimate Urban Community Housing Price—A Case Study in Wuhan, China
Previous Article in Journal
Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments
Previous Article in Special Issue
A Dark Target Detection Method Based on the Adjacency Effect: A Case Study on Crack Detection
Open AccessArticle

Anisotropic Diffusion Based Multiplicative Speckle Noise Removal

1
School of Information Science and Technology, Northwest University, Xi’an 710127, China
2
School of Science, Xi’an Technological University, Xi’an 710021, China
3
Department of Mathematics, Xidian University, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3164; https://doi.org/10.3390/s19143164
Received: 17 June 2019 / Revised: 6 July 2019 / Accepted: 13 July 2019 / Published: 18 July 2019
(This article belongs to the Special Issue Advance and Applications of RGB Sensors)
Multiplicative speckle noise removal is a challenging task in image processing. Motivated by the performance of anisotropic diffusion in additive noise removal and the structure of the standard deviation of a compressed speckle noisy image, we address this problem with anisotropic diffusion theories. Firstly, an anisotropic diffusion model based on image statistics, including information on the gradient of the image, gray levels, and noise standard deviation of the image, is proposed. Although the proposed model can effectively remove multiplicative speckle noise, it does not consider the noise at the edge during the denoising process. Hence, we decompose the divergence term in order to make the diffusion at the edge occur along the boundaries rather than perpendicular to the boundaries, and improve the model to meet our requirements. Secondly, the iteration stopping criteria based on kurtosis and correlation in view of the lack of ground truth in real image experiments, is proposed. The optimal values of the parameters in the model are obtained by learning. To improve the denoising effect, post-processing is performed. Finally, the simulation results show that the proposed model can effectively remove the speckle noise and retain minute details of the images for the real ultrasound and RGB color images. View Full-Text
Keywords: anisotropic diffusion; partial differential equations (PDE); multiplicative noise removal anisotropic diffusion; partial differential equations (PDE); multiplicative noise removal
Show Figures

Figure 1

MDPI and ACS Style

Gao, M.; Kang, B.; Feng, X.; Zhang, W.; Zhang, W. Anisotropic Diffusion Based Multiplicative Speckle Noise Removal. Sensors 2019, 19, 3164.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop