Importance of Noise Reduction and Suppression of Artifacts in Restoration Techniques: A State-of-the-Art. Nadia Dahraoui #*1 , M’hamed Boulakroune *2 , Djamel Benatia #3 # Electronics Department, Faculty of Engineer Sciences, University Hadj Lakhdar of Batna, Batna, Algeria 1 dahraouinadia@gmail.com 3 Dj_benatia@yahoo.fr * Electronics and Communication Department, Faculty of New Technologies of Information and Communication, University Kasdi Merbah of Ouargla, Ouargla, Algeria 2 Boulakroune.mhamed@univ-ouargla.dz Abstract— The Removal of noise and restoration of signals has been one of the most interesting researches in the field of signal processing in the past few year. In this paper, we have tested various deconvolution algorithms proposed in literature, using denoised signal (by wavelets techniques in our case) instead of measured one which is the real signal degraded by measurement procedure. It is very difficult to compare algorithms because the results obtained depend heavily on signal quality (signal-to-noise ratio, sampling), and on algorithm parameters and optimizations. Which criteria should be used to compare signals? Our algorithm which based on Tikhonov-Miller regularization and a model of solution, is a iterative algorithm, gives best results without artifacts and oscillations related to noise, and achieves higher-quality denoising and a high restoration ratio for noisy signal than the existing methods. Keywords— Resoration technique, SIMS depth profiling, DWT, Wavelet tresholding, Denoising signal, Regularization tools, Multiresolution deconvolution. I. INTRODUCTION The Deconvolution methods are used in several electron spectroscopies to improve the experimental results which are masked by instrumental effects and by physical processes involved in the measurements. In Secondary Ion Mass Spectrometry depth profiles, deconvolution methods have been employed to approximate the measured composition profiles to the supposedly original profiles. Secondary ion mass spectrometry (SIMS) is widely used for the measurement of doping, impurity and matrix profiles in semiconductors. In this application, the concentration data required may span 10 orders of magnitude overall, and 4-6 orders for a particular species [1-3]. As the SIMS data do not directly represent the true element profile, a data quantification procedure is necessary. Various methods from simple linear mapping (ion dose to depth, signal to concentration) to deconvolution using response functions of various types [3-5]. Due to the complexity of the dynamic SIMS profile process and the large record dynamic range required, a very careful and unbiased treatment of the measured SIMS data is vital to the success of a deconvolution method to be used. The deconvolution of depth profiling data in SIMS analysis amounts to the solution of an appropriate ill-posed problem in that any random noise in data leads to no unique and no stable solution (oscillatory signal with negative components, which are physically not acceptable in SIMS analysis). Thus, the results must be regularized. Our algorithm based on the Tikhonov-Miller regularization [6]. In this study, we evaluate a few well-known deconvolution algorithms and their modifications . specific attention is given to the comparaison of the deconvolution based on measured profiles and other based on denoised profiles. The simplist approach to deconvolution is the inverse filtering in which the discrete Fourier Transform (DFT) of the true signal is estimated. This approach leads to excessive noise amplification. Another linear approach is based on the linear Wiener Filtering. Therefore, other classes of iterative deconvolution techniques will studied. II. EXPERIMENTAL Secondary-ion mass spectrometry (SIMS) is a technique used to analyze the composition of solid surfaces and thin films by sputtering the surface of the specimen with a focused primary ion beam and collecting and analyzing ejected secondary ions. When acquiring a depth profile, the secondary ions are emitted discontinuously. It is the control electronics that manages the counting of the secondary ions striking the detector, and this is done discretely over time. The SIMS signals are thus discrete signals of finite duration. In this case, the transformation of a continuous signal into a discrete signal, that is to say the sampling problem, does not arise. The SIMS Copyright IPCO-2017 ISSN 2356-5608 5th International Conference on Control Engineering&Information Technology (CEIT-2017) Proceeding of Engineering and Technology –PET Vol.32 pp.32-36