One of the key issues in removing random-valued impulse noise from digital images using switching filters is the impulse noise detection. Impulse noise is a random, spiked variation in the brightness of the image. In this paper, a new impulse noise detection algorithm is presented that is based on Noise ratio Estimation and a combination of K-means clustering and Non-Local Means based filter (N...

background: drug resistance is widely believed to be an increasingly serious threat to global public health. we have previously reported that short term exposure of microorganisms to diagnostic ultrasound waves could significantly alter their sensitivity to antibiotics. in our previous studies, klebsiella pneumoniae showed major differences in the sensitivity to antibiotics in exposed and non-e...

background: whether exposure to common electromagnetic fields affects human health adversely has been a controversial issue. the main goal of this study was to assess the role of 900 mhz microwave radiations with different specific absorption rates (sars), emitted from some widely used cell phones, on the induction of adaptive response in male balb/c mice after receiving a lethal dose of gamma ...

We present a new clustering algorithm called k-means-u* which in many cases is able to significantly improve the clusterings found by k-means++, the current de-facto standard for clustering in Euclidean spaces. First we introduce the k-means-u algorithm which starts from a result of k-means++ and attempts to improve it with a sequence of non-local “jumps” alternated by runs of standard k-means....

We describe an algorithm for locally-adaptive parameter estimation of spatially inhomogeneous Markov random elds (MRFs). In particular, we establish that there is a unique solution which maximizes the local pseudo-likelihood in the inhomogeneous MRF model. Subsequently we demonstrate how Besag's iterative conditional mode (ICM) procedure can be generalized from homogeneous MRFs to inhomogeneous...