Absorption and scattering of light from water, and also in-water particles, are two main factors which impose the blurring and noise on captured images from underwater objects. In this manner, the working range of underwater optical imaging (UOI) systems is limited by these phenomena. Wavelet de-noised deconvolution and Fourier regularized deconvolution are two well-known software methods which have already been used for restoring the blurred-noisy underwater images. Nevertheless, the results of both of these software methods are very noise sensitive. Hybrid Fourier wavelet deconvolution is another image processing method which simultaneously benefits from the advantages of both wavelet de-noised deconvolution and Fourier regularized deconvolution techniques. In this paper, we propose to employ the hybrid Fourier wavelet method for underwater images restoration. Computer simulations show the superior performance of this technique to utilize in the UOI systems in comparison with the previously used wavelet de-noised deconvolution and Fourier regularized deconvolution methods.

Introduction: Glioma grading is an important clinical procedure that determines the procedure of the patient's treatment. There is an increasing interest in other complementary techniques in addition to histo-pathological evaluation as the gold standard method. Perfusion indices have shown promising correlations with histo-pathological grades and neovascularization degrees in gliomas. In this study, the processing times and classification performances of cerebral blood flow (CBF) parameters calculated using singular value decomposition (SVD) and Fourier deconvolution methods were evaluated. Materials and Methods: The statistical differences between the CBF magnitudes of the high-and low-grade gliomas were evaluated for eighteen patients with pathologically proven gliomas (Low-grade gliomas, 5,High-grade gliomas, 13). The classification performances of the calculated indices were evaluated using the receiver operating curve analysis. Results: The deconvolution processing times for the SVD method were significantly higher than those of the Fourier deconvolution method (about ~9 times). There were statistically significant differences in both CBF indices between the high-and low-grade gliomas (P<0. 05). In ROC curve analysis, CBF indices calculated by the SVD method had a higher area under the curve (about 8538. 5 vs. 8153. 8). Conclusion: Based on the study, the glioma grade might be determined using the Fourier-based CBF index with an almost similar grading efficiency and a much lower processing time than the CBF index calculated by the SVD method.

Introduction: Registration of scattered photons along with the primary photons is a major cause of degrading image quality and quantitative accuracy in single photon emission tomography. To deal with the problem some techniques have been developed. deconvolution is the technique that considers the effect of scatter as convolution of the primary photons with a scatter function. Therefore the image of the non-scattered photons could be extracted from a mixed image of scattered and non-scattered photons using a deconvolution technique. Methods and materials: In this study the complex wavelet wiener deconvolution was used as a method of deconvolution. We used Monte Carlo generated phantoms to simulate the scattered and scatter free Images. Results: This simulated study showed that the method improves the image quality and quantitative accuracy up to 1.5 dB in comparison to scatter reduction in the energy window of 20%. Conclusion: Complex wavelet wiener deconvolution has the potential to be used as an efficient scatter compensation method. Other deconvolution methods such as iterative may also be used on the high pass sub-bands components of complex wavelet decomposition as a complementary method.      

In order to increase the signal/noise ratio, one of the important challenges in seismic data processing is suppression of random noises. In this paper f-x deconvolution or deconvolution in frequency-space domain is employed for reduction of random noises from seismic section. This is based on data transformation from time-space domain to frequency-space domain. Seismic events are correlatable along x-direction from trace to trace but random noises are not. In fact f-x deconvolution is able to predict coherent events from trace to trace in space direction. In this paper a computer code for f-x deconvolution has been written. This program applied on shot records and zero offset section with different levels of random noises. Consequently, the ability of f-x deconvolution in reduction of random noises has been proved.

deconvolution is considered as a successful tool in seismic exploration for increasing the temporal resolution of the data. Gabor deconvolution is proposed to treat the non-stationarity issue of the problem by breaking it into several stationary sub-problems via a Gaussian window, solving them independently, and then, recombining/projecting the sub-solutions into an approximate solution to the original nonstationary problem. The projected Gabor deconvolution has recently been proposed by the second author as an improvement over Gabor deconvolution. In the projected Gabor deconvolution, the sub-problems are projected to a unified problem in time domain, and then, the resulting problem is solved. This modification brings useful advantages over the Gabor deconvolution including an improved convergence property, more efficiency for sparse deconvolution, more flexibility for incorporating prior information in the presence of noise, and more reflectivity structure via a least-squares method. In this paper, we propose a method for sparse and non-sparse deconvolution of non-stationary seismic signals in the presence of Gaussian and spike-like random noises. Numerical tests using simulated and field data are presented to show high performance of the proposed method for generating accurate and stable reflectivity models from nonstationary seismograms....

Let G be a locally compact group and w be a symmetric weight function on G. We define a co-product Gw on the weighted algebra L¥ (G, w-1) of essentially w-bounded Borel measurable functions on G and show that L¥ (G, w-1) becomes a Kac algebra with natural co-inverse kw and Haar weight  fw. We use the machinery of Kac algebras to introduce the weighted Fourier and Fourier-Stieltjes algebra A(G, w-1) and B(G, w-1) of G.

Background and the purpose of the study: Affinity-based target deconvolution is an emerging method for the identification of interactions between drugs/drug candidates and cellular proteins, and helps to predict potential activities and side effects of a given compound. In the present study, we hypothesized that a part of safranal pharmacological effects, one of the major constituent of Crocus sativus L., relies on its physical interaction with target proteins.Methods: Affinity chromatography solid support was prepared by covalent attachment of safranal to agarose beads.After passing tissue lysate through the column, safranal-bound proteins were isolated and separated on SDS-PAGE or two-dimensional gel electrophoresis. Proteins were identified using MALDI-TOF/TOF mass spectrometry and Mascot software.Results and major conclusion: Data showed that safranal physically binds to beta actin, cytochrome b-c1 complex sub-unit 1, trifunctional enzyme sub-unit beta and ATP synthase sub-unit alpha and beta. These interactions may explain part of safranal’s pharmacological effects. However, phenotypic and/or biological relevance of these interactions remains to be elucidated by future pharmacological studies.

Due to the spherical divergence and specifically absorption in the earth, amplitude of a propagating seism wave varies as a function of time. This stretches the wavelet in time and reduces the time (or vertical) resolution of the seismic sections. To overcome the problem one has to apply so called spatial migration or deconvolution on data. Usually the least square Wiener deconvolution is used to boost up the attenuated frequency components. Unfortunately, the Wiener based deconvolution methods assume that the source generated seismic wavelet is stationary (i.e. its frequency content remains unchanged within the record). A method of deconvolution in the Gabor domain is applied in this paper that considers the seismic data as a non-stationary phenomenon.The Gabor transform (Equation 1) is a windowed or short time Fourier transform, where the window used to isolate the frequency content of input record in time, is a Gaussian type. According to the uncertainty principle, the Gabor transform has the least uncertainty among other windowed Fourier transforms.Please clik on PDF icon to viwe the complet abstract.

A genetic based algorithm for deconvolution of Printed Circuit Board (PCB) thermal images is presented.The deconvolution of thermal images is modeled as an optimization problem, whose cost function is to be minimized based on mechanics of natural selection and genetics. The proposed algorithm can be configured with all available apriori information to speedup the solution computation. The paper presents the results for deconvolution using the proposed genetic algorithm and its utility in PCB infrared thermal testing.