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...

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...

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 ...

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...

In this paper, we investigate the use of the non-local means (NLM) denoising approach in the context of image deblurring and restoration. We propose a novel deblurring approach that utilizes a non-local regularization constraint. Our interest in the NLM principle is its potential to suppress noise while effectively preserving edges and texture detail. Our approach leads to an iterative cost fun...

There exist different kind of techniques to remove unwanted signals from images. The major denoising methods include filtering technique which is under the category of neighbourhood methods.The drawback of neighbourhood method is that tend to lose fine details of the image so that blurring may occur.Noise reduction and preservation of actual image can be done efficiently if we extend the consid...

We present a novel spatiotemporal-adaptive Multiscale Finite Volume (MsFV) method, which is based on the natural idea that the global coarse-scale problem has longer characteristic time than the local fine-scale problems. As a consequence, the global problem can be solved with larger time steps than the local problems. In contrast to the pressure-transport splitting usually employed in the stan...

We provide a simple example that illustrates the advantage of adaptive over non-adaptive strategies for quantum channel discrimination. In particular, we give a pair of entanglementbreaking channels that can be perfectly discriminated by means of an adaptive strategy that requires just two channel evaluations, but for which no non-adaptive strategy can give a perfect discrimination using any fi...

This paper offers a new technique for spatially adaptive estimation. The local likelihood is exploited for nonparametric modeling of observations and estimated signals. The approach is based on the assumption of a local homogeneity of the signal: for every point there exists a neighborhood in which the signal can be well approximated by a constant. The fitted local likelihood statistics are use...