Elimination of Thermally Generated Charge in Charged Coupled Devices Using Bayesian Estimator

This paper deals with advanced methods for elimination of thermally generated charge in astronomical images, which were acquired by a Charged Coupled Device (CCD) sensor. There exist a number of light images acquired by telescope, which were not corrected by dark frame. The reason is simple: the dark frame doesn’t exist, because it was not acquired. This situation may for instance come when sufficient memory space is not available. Correction methods based on the modeling of the light and dark image in the wavelet domain will be discussed. As the model for the dark frame image and for the light image the generalized Laplacian was chosen. The model parameters were estimated using moment method, whereas an extensive measurement on an astronomical camera was proposed and done. This measurement simplifies estimation of the dark frame model parameters. Finally a set of astronomical testing images was corrected and then the objective criteria for an image quality evaluation based on the aperture photometry were applied

Point Spread Functions in Identification of Astronomical Objects from Poisson Noised Image

This article deals with modeling of astronomical objects, which is one of the most fundamental topics in astronomical science. Introduction part is focused on problem description and used methods. Point Spread Function Modeling part deals with description of basic models used in astronomical photometry and further on introduction of more sophisticated models such as combinations of interference, turbulence, focusing, etc. This paper also contains a~way of objective function definition based on the knowledge of Poisson distributed noise, which is included in astronomical data. The proposed methods are further applied to real astronomical data

Modeling of Scientific Images Using GMM

This paper deals with modeling of scientific and multimedia images in the wavelet domain. Images transformed into wavelet domain have a special shape of probability density function (PDF). Thus wavelet coefficients PDFs are usually modeled using generalized Laplacian PDF model (GLM), which is characterized by two parameters. The wavelet coefficients modeling can be more efficient, while the Gaussian mixture model (GMM) is utilized. GMM model is given by addition of at least two Gaussian PDFs with different standard deviations. The equation system derived by moment method for GMM model parameters estimation will be presented. The equation system was derived for an addition of two GMM models. So it is suitable for advanced denoising systems, where an addition of two GMM random variables is considered (e.g. dark current). This paper presents a continuing of previous work [11], deals with dark current elimination (novel approach) and shows a better way of to modeling light image and dark current

Measurement and Analysis of Real Imaging Systems

This paper is devoted to statistical analysis of noise generated in real imaging systems and noise suppression methods. The introductory part is focused on description of imaging systems, image degradations, and noise types present in them. The noise analysis section includes determination of basic noise characteristics, the probability distribution and dependence on the signal. The described methods are used to compare properties of two digital still cameras: Nikon D70 and Canon EOS 500D and video camera: JAI CM-040GE. The section devoted to noise suppression discusses different methods of wavelet coefficients thresholding and threshold estimation. The wavelet coefficients are produced by two forms of the wavelet transform: the discrete wavelet transform and the dual-tree complex wavelet transform. The described noise suppression methods are applied to the data sets which were acquired by the analyzed systems under poor lighting conditions

Noise Analysis of MAIA System and Possible Noise Suppression

This paper is devoted to the noise analysis and noise suppression in a system for double station observation of the meteors now known as MAIA (Meteor Automatic Imager and Analyzer). The noise analysis is based on acquisition of testing video sequences in different light conditions and their further statistical evaluation. The main goal is to find a suitable noise model and subsequently determine if the noise is signal dependent or not. Noise and image model in the wavelet domain should be based on Gaussian mixture model (GMM) or Generalized Laplacian Model (GLM) and the model parameters should be estimated by moment method. Furthermore, noise should be modeled by GMM or GLM also in the space domain. GMM and GLM allow to model various types of probability density functions. Finally the advanced denoising algorithm using Bayesian estimator is applied and its performance is verified

Speech disorder and vocal tremor in postural instability/gait difficulty and tremor dominant subtypes of Parkinson’s disease

Hypokinetic dysarthria is a multidimensional impairment affecting all main speech subsystems with variable patterns and severity across individual Parkinson's disease (PD) patients. We can thus assume that inter-individual abnormal speech patterns are related to the various clinical subtypes of PD with different prominent motor symptoms. The aim of this cross-sectional study was to compare speech disorder between patients with the postural instability/gait difficulty (PIGD) and tremor-dominant (TD) motor phenotypes of PD. Speech samples were acquired from a total of 63 participants, including 21 PIGD patients, 21 TD patients, and 21 healthy controls. Quantitative acoustic vocal assessment of 12 unique speech dimensions related to phonation, vocal tremor, oral diadochokinesis, articulation, prosody and speech timing was performed. Speech impairment was more pronounced in the PIGD group than in the TD group, with an area under the curve of 0.76. Patients in the PIGD group manifested abnormalities in pitch breaks, articulatory decay, decreased rate of follow-up speech segments and inappropriate silences, apart from monopitch and irregular AMR that were affected in TD group as well. An abnormal vocal tremor was present in only 10% of PD patients, with no differences between the PD phenotypes. We found a correlation between non-motor symptom severity and speech timing (r = − 0.40, p = 0.009). The present study demonstrates that speech disorder reflects the underlying motor phenotypes. Vocal tremor appeared to be an isolated phenomenon that does not share similar pathophysiology with limb tremor