Next Article in Journal
Representation by Chebyshev Polynomials for Sums of Finite Products of Chebyshev Polynomials
Previous Article in Journal
Symmetry Identities of Changhee Polynomials of Type Two
Open AccessArticle

A Novel Adaptive Non-Local Means-Based Nonlinear Fitting for Visibility Improving

1
Shanxi Engineering Research Center for Road Intelligent Monitoring, Shanxi Transportation Research Institute, Taiyuan 030006, China
2
Key Lab of highway Construction & Maintenance Technology in Loess Region, Shanxi Transportation Research Institute, Taiyuan 030006, China
3
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Symmetry 2018, 10(12), 741; https://doi.org/10.3390/sym10120741
Received: 7 November 2018 / Revised: 2 December 2018 / Accepted: 6 December 2018 / Published: 11 December 2018
The spatial-based method has become the most widely used method in improving the visibility of images. The visibility improving is mainly to remove the noise in the image, in order to trade off denoising and detail maintaining. A novel adaptive non-local means-based nonlinear fitting method is proposed in this paper. Firstly, according to the smoothness of the intensity around the central pixel, eight kinds of templates with different precision are exploited to approximate the central pixel through a novel adaptive non-local means filter design; the approximate weight coefficients of templates are derived from the approximation credibility. Subsequently, the fractal correction is used to smooth the denoising results. Eventually, the Rockafellar multiplier method is employed to generalize the smooth plane fitting to any geometric surface, thus yielding the optimal fitting of the center pixel approximation. Through a large number of experiments, it is clearly elucidated that compared with the classical spatial iteration-based methods and the recent denoising algorithms, the proposed algorithm is more robust and has better effect on denoising, while keeping more original details during denoising. View Full-Text
Keywords: denoising; detail maintaining; non-local means; approximation; optimal denoising; detail maintaining; non-local means; approximation; optimal
Show Figures

Figure 1

MDPI and ACS Style

Wu, H.; Jia, L.; Meng, Y.; Liu, X.; Lan, J. A Novel Adaptive Non-Local Means-Based Nonlinear Fitting for Visibility Improving. Symmetry 2018, 10, 741.

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