This paper focuses on sequential Monte Carlo approximations of smoothing distributions in conditionally linear and Gaussian state spaces. To reduce Monte Carlo variance of smoothers, it is typical in these models to use Rao-Blackwellization: particle approximation is used to sample sequences of hidden regimes while the Gaussian states are explicitly integrated conditional on the sequence of reg...

The present paper focuses on a new class of mesh filter for grayscale images, called grid smoothing filter. The framework presented considers an image as a sampling grid associated to a set of gray levels. Furthermore, the sampling grid is seen as mesh composed by vertices and edges, the number of vertices being equal to the number of pixels in the image. Embedding the mesh in a 2D Euclidian sp...

The analysis of functional magnetic resonance imaging (fMRI) data involves multiple stages of data pre-processing before the activation can be statistically detected. Spatial smoothing is a very common pre-processing step in the analysis of functional brain imaging data. This study presents a broad perspective on the influence of spatial smoothing on fMRI group activation results. The data obta...

The multiplicative nature of speckle noise present in imaging modalities like echocardiography complicates the despeckling procedure as it would be necessary to remove noise with the edges well preserved. A novel speckle reduction technique based on integration of moving average filter using fuzzy triangulation membership function (TMAV) with moving average center with wiener filter is proposed...

In order to reduce the estimated errors of the inertial navigation system (INS)/Wireless sensor network (WSN)-integrated navigation for mobile robots indoors, this work proposes an on-line iterated extended Rauch-Tung-Striebel smoothing (IERTSS) utilizing inertial measuring units (IMUs) and an ultrasonic positioning system. In this mode, an iterated Extended Kalman filter (IEKF) is used in forw...

In this paper, we propose a sharpness dependent filter design based on the fairing of surface normal, whereby the filtering algorithm automatically selects a filter. This may be a mean-filter, a min-filter, or a filter ranked between these two, depending on the local sharpness value and the sharpness dependent weighting function selected. To recover the original shape of a noisy model, the algo...

The effectiveness of Savitzky–Golay type symmetric polynomial smoothers is known to be strongly dependent on the window size. Many authors note that selection of the appropriate window size is essential for achieving the correct trade-off between noise reduction and avoiding the introduction of bias. However, it is often overlooked that, in the case of non-stationary signals, the optimal window...

It is well-known that local filtering-based edge preserving smoothing method suffers from halo artifacts. In this paper, a weighted guided image filter is introduced by incorporating an edge-aware weighting into an accessible guided image filter to address the problem. The WGIF inherit benefits of both global and local smoothing filters in the sense that: 1) the difficulty of the WGIF is O(N) f...

This paper introduces a new smoothing algorithm for time series processing to be implemented in various applications, including, among others, array processing and wireless communication. This new approach has also been tested for noise filtering and edge preserving in images. The algorithm is based on a nonlinear exponential smoothing (NLES) method that shows a significant improvement in the m...

A popular smoothing technique commonly used in time series analysis is double exponential smoothing. Basically, it’s an improvement of simple exponential smoothing which does the exponential filter process twice. Many researchers had developed the technique, hence Brown’s double exponential smoothing and Holt’s double exponential smoothing. Here, we introduce a new approach of double exponentia...