Browsing by Subject "De-noising"

Now showing 1 - 5 of 5

Open Access
Denoising images corrupted by impulsive noise using projections onto the epigraph set of the total variation function (PES-TV)
(Springer U K, 2015) Tofighi M.; Kose, K.; Çetin, A. Enis
In this article, a novel algorithm for denoising images corrupted by impulsive noise is presented. Impulsive noise generates pixels whose gray level values are not consistent with the neighboring pixels. The proposed denoising algorithm is a two-step procedure. In the first step, image denoising is formulated as a convex optimization problem, whose constraints are defined as limitations on local variations between neighboring pixels. We use Projections onto the Epigraph Set of the TV function (PES-TV) to solve this problem. Unlike other approaches in the literature, the PES-TV method does not require any prior information about the noise variance. It is only capable of utilizing local relations among pixels and does not fully take advantage of correlations between spatially distant areas of an image with similar appearance. In the second step, a Wiener filtering approach is cascaded to the PES-TV-based method to take advantage of global correlations in an image. In this step, the image is first divided into blocks and those with similar content are jointly denoised using a 3D Wiener filter. The denoising performance of the proposed two-step method was compared against three state-of-the-art denoising methods under various impulsive noise models.
Open Access
Empirical mode decomposition aided by adaptive low pass filtering
(IEEE, 2012) Öztürk, Onur; Arıkan, Orhan; Çetin, A. Enis
Empirical Mode Decomposition (EMD) is an adaptive signal analysis technique which derives its basis functions from the signal itself. EMD is realized through successive iterations of a sifting process requiring local mean computation. For that purpose, local minima and maxima of the signal are assumed to constitute proper local time scales. EMD lacks accuracy, however, experiencing the so-called mode mixing phenomenon in the presence of noise which creates artificial extrema. In this paper, we propose adaptively filtering the signal in Discrete Cosine Transform domain before each local mean computation step to prevent mode mixing. Denoising filter thresholds are optimized for a product form criterion which is a function of the preserved energy and the eliminated number of extrema of the signal after filtering. Results obtained from synthetic signals reveal the potential of the proposed technique. © 2012 IEEE.
Open Access
Filtered Variation method for denoising and sparse signal processing
(IEEE, 2012) Köse, Kıvanç; Cevher V.; Çetin, A. Enis
We propose a new framework, called Filtered Variation (FV), for denoising and sparse signal processing applications. These problems are inherently ill-posed. Hence, we provide regularization to overcome this challenge by using discrete time filters that are widely used in signal processing. We mathematically define the FV problem, and solve it using alternating projections in space and transform domains. We provide a globally convergent algorithm based on the projections onto convex sets approach. We apply to our algorithm to real denoising problems and compare it with the total variation recovery. © 2012 IEEE.
Open Access
Range-doppler radar target detection using denoising within the compressive sensing framework
(IEEE, 2014-09) Sevimli, R. Akın; Tofighi, Mohammad; Çetin, A. Enis
Compressive sensing (CS) idea enables the reconstruction of a sparse signal from a small set of measurements. CS approach has applications in many practical areas. One of the areas is radar systems. In this article, the radar ambiguity function is denoised within the CS framework. A new denoising method on the projection onto the epigraph set of the convex function is also developed for this purpose. This approach is compared to the other CS reconstruction algorithms. Experimental results are presented1. © 2014 EURASIP.
Open Access
Using a variation of empirical mode decomposition to remove noise from signals
(IEEE, 2011-06) Kaleem, M. F.; Guergachi, A.; Krishnan, S.; Çetin, A. Enis
This paper will describe the application of -based decomposition, which is a variation of the empirical mode decomposition method based on modified peak selection, to de-noising and de-trending of signals. The -based decomposition method will be explained, and its application to synthetic and real-world signals in the context of de-noising and de-trending will be described. Comparison between the computational simplicity of the τ-based decomposition method to de-noising and de-trending of signals and approaches based on empirical mode decomposition will be highlighted. © 2011 IEEE.