Non-Local Means (NLM) and its variants have proven to be effective and robust in many image denoising tasks. In this letter, we study approaches to selecting center pixel weights (CPW) in NLM. Our key contributions are: 1) we give a novel formulation of the CPW problem from a statistical shrinkage perspective; 2) we construct the James-Stein shrinkage estimator in the CPW context; and 3) we pro...

| The NASA scatterometer (NSCAT) collected Ku-band scatterometer measurements from Sept. 1996 to June 1997. These data are converted high resolution six day images of the polar regions through the use of the scatterometer image reconstruction with lter (SIRF) algorithm. SIRF produces images of A and B where A is o at 40 incidence and B is the incidence angle dependence of . A simple four-dimens...

In this paper, an efficient image deblurring algorithm is proposed. This algorithm restores the blurred image by incorporating a curvelet-based empirical Wiener filter with a spatial-based joint non-local means filter. Curvelets provide a multidirectional and multiscale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditiona...

Large quantities of data are emerging every year and an accurate clustering algorithm is needed to derive information from these data. K-means clustering algorithm is popular and simple, but has many limitations like its sensitivity to initialization, provides local optimum solutions. K-harmonic means clustering is an improved variant of K-means which is insensitive to the initialization of cen...

Many applications of clustering require the use of normalized data, such as text or mass spectra mining. The spherical k-means algorithm [6], an adaptation of the traditional k-means algorithm, is highly useful for data of this kind because it produces normalized cluster centers. The k-medians clustering algorithm is also an important clustering tool because of its wellknown resistance to outli...

Image denoising approaches have attracted many researchers. The main tackled problem is the removal of additive Gaussian noise. However, it is very important to expand the filters capacity to other types of noise, for example the multiplicative noise of SAR images. The state of the art methods in this area work with patch similarity. This paper shows a new approach for speckle removal based on ...

The K-Means Clustering Approach is one of main algorithms in the literature of Pattern recognition and Machine Learning. Yet, due to the random selection of cluster centers and the adherence of results to initial cluster centers, the risk of trapping into local optimality ever exists. In this paper, inspired by a genetic algorithm which is based on the K-means method , a new approach is develop...

In this paper, a novel remote sensing image enhancement technique based on a non-local means filter in a nonsubsampled contourlet transform (NSCT) domain is proposed. The overall flow of the approach can be divided into the following steps: Firstly, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands with NSCT. Secondly, contrast stretching is adopted to...

We propose a new image denoising algorithm when the data is contaminated by a Poisson noise. As in the Non-Local Means filter, the proposed algorithm is based on a weighted linear combination of the observed image. But in contract to the latter where the weights are defined by a Gaussian kernel, we propose to choose them in an optimal way. First some ”oracle” weights are defined by minimizing a...

The mechanism of gene regulation has been studied intensely for decades. It is important to identify synergistic transcriptional motifs. Its search space is so large that an efficient computational method is required. In this paper, we present the method that can search automatically both transcriptional motif list and gene expression profiles for synergistic motif combinations. It uses evoluti...