Open AccessImage denoising using generalised Cauchy filter
Author(s) -
Karami Azam,
Tafakori Laleh
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.0554
Subject(s) - cauchy distribution , noise reduction , mathematics , noise (video) , convolution (computer science) , filter (signal processing) , non local means , algorithm , distribution (mathematics) , pattern recognition (psychology) , image (mathematics) , function (biology) , image denoising , mathematical optimization , artificial intelligence , computer science , mathematical analysis , computer vision , evolutionary biology , artificial neural network , biology
In many image processing analysis, it is important to significantly reduce the noise level. This study aims at introducing an efficient method for this purpose based on generalised Cauchy (GC) distribution. Therefore, some characteristics of GC distribution is considered. In particular, the characteristic function of a GC distribution is derived by using the theory of positive definite densities and utilising the density of a GC random variable as the characteristic function of a convolution of two generalised non‐symmetric Linnik variables. Further, GC distribution is considered as a filter and in the proposed method for image noise reduction the optimal parameters of GC filter is defined by using the particle swarm optimisation. The proposed method is applied to different types of noisy images and the obtained results are compared with four state‐of‐the‐art denoising algorithms. Experimental results confirm that their method could significantly reduce the noise effect.