Image analysis, modeling, enhancement, restoration, feature extraction and their applications in nondestructive evaluation and radio astronomy

The principal topic of this dissertation is the development and application of signal and image processing to Nondestructive Evaluation (NDE) and radio astronomy;The dissertation consists of nine papers published or submitted for publication. Each of them has a specific and unique topic related to signal processing or image processing in NDE or radio astronomy. Those topics are listed in the following. (1) Time series analysis and modeling of Very Large Array (VLA) phase data. (2) Image analysis, feature extraction and various applied enhancement methods for industrial NDE X-ray radiographic images. (3) Enhancing NDE radiographic X-ray images by adaptive regional Kalman filtering. (4) Robotic image segmentation, modeling, and restoration with a rule based expert system. (5) Industrial NDE radiographic X-ray image modeling and Kalman filtering considering signal-dependent colored noise. (6) Computational study of Kalman filtering VLA phase data and its computational performance on a supercomputer. (7) A practical and fast maximum entropy deconvolution method for deblurring industrial NDE X-ray and infrared images. (8) Local feature enhancement of synthetic radio images by adaptive Kalman filtering. (9) A new technique for correcting phase data of a synthetic-aperture antenna array

Factor Graph Based LMMSE Filtering for Colored Gaussian Processes

We propose a low complexity, graph based linear minimum mean square error (LMMSE) filter in which the non-white characteristics of a random process are taken into account. Our method corresponds to block LMMSE filtering, and has the advantage of complexity linearly increasing with the block length and the ease of incorporating the a priori information of the input signals whenever possible. The proposed method can be used with any random process with a known autocorrelation function with the help of an approximation to an autoregressive (AR) process. We show through extensive simulations that our method performs very close to the optimal block LMMSE filtering for Gaussian input signals.Comment: 5 pages, 4 figure

Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

The localized nature of curvelet functions, together with their frequency and dip characteristics, makes the curvelet transform an excellent choice for processing seismic data. In this work, a denoising method is proposed based on a combination of the curvelet transform and a whitening filter along with procedure for noise variance estimation. The whitening filter is added to get the best performance of the curvelet transform under coherent and incoherent correlated noise cases, and furthermore, it simplifies the noise estimation method and makes it easy to use the standard threshold methodology without digging into the curvelet domain. The proposed method is tested on pseudo-synthetic data by adding noise to real noise-less data set of the Netherlands offshore F3 block and on the field data set from east Texas, USA, containing ground roll noise. Our experimental results show that the proposed algorithm can achieve the best results under all types of noises (incoherent or uncorrelated or random, and coherent noise)

A Low-Complexity Graph-Based LMMSE Receiver Designed for Colored Noise Induced by FTN-Signaling

We propose a low complexity graph-based linear minimum mean square error (LMMSE) equalizer which considers both the intersymbol interference (ISI) and the effect of non-white noise inherent in Faster-than-Nyquist (FTN) signaling. In order to incorporate the statistics of noise signal into the factor graph over which the LMMSE algorithm is implemented, we suggest a method that models it as an autoregressive (AR) process. Furthermore, we develop a new mechanism for exchange of information between the proposed equalizer and the channel decoder through turbo iterations. Based on these improvements, we show that the proposed low complexity receiver structure performs close to the optimal decoder operating in ISI-free ideal scenario without FTN signaling through simulations.Comment: 6 pages, 6 figures, IEEE Wireless Communications and Networking Conference 2014, Istanbul, Turke

Spatial deconvolution of spectropolarimetric data: an application to quiet Sun magnetic elements

Observations of the Sun from the Earth are always limited by the presence of the atmosphere, which strongly disturbs the images. A solution to this problem is to place the telescopes in space satellites, which produce observations without any (or limited) atmospheric aberrations. However, even though the images from space are not affected by atmospheric seeing, the optical properties of the instruments still limit the observations. In the case of diffraction limited observations, the PSF establishes the maximum allowed spatial resolution, defined as the distance between two nearby structures that can be properly distinguished. In addition, the shape of the PSF induce a dispersion of the light from different parts of the image, leading to what is commonly termed as stray light or dispersed light. This effect produces that light observed in a spatial location at the focal plane is a combination of the light emitted in the object at relatively distant spatial locations. We aim to correct the effect produced by the telescope's PSF using a deconvolution method, and we decided to apply the code on Hinode/SP quiet Sun observations. We analyze the validity of the deconvolution process with noisy data and we infer the physical properties of quiet Sun magnetic elements after the deconvolution process.Comment: 14 pages, 9 figure