Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

A General Filter for Stretched-Grid Models: Application in Two-Dimension Polar Geometry

Variable-resolution grids are used in global atmospheric models to improve the representation of regional scales over an area of interest: they have reduced computational cost compared to uniform high-resolution grids, and avoid the nesting issues of limited-area models. To address some concerns associated with the stretching and anisotropy of the variable-resolution computational grid, a general convolution filter operator was developed.\ud \ud The convolution filter that was initially applied in Cartesian geometry in a companion paper is here adapted to cylindrical polar coordinates as an intermediate step toward spherical polar latitude–longitude grids. Both polar grids face the so-called “pole problem” because of the convergence of meridians at the poles.\ud \ud In this work the authors will present some details related to the adaptation of the filter to cylindrical polar coordinates for both uniform as well as stretched grids. The results show that the developed operator is skillful in removing the extraneous fine scales around the pole, with a computational cost smaller than that of common polar filters. The results on a stretched grid for vector and scalar test functions are satisfactory and the filter’s response can be optimized for different types of test function and noise one wishes to remove

Impact of Spatial Filtering on Distortion from Low-Noise Amplifiers in Massive MIMO Base Stations

In massive MIMO base stations, power consumption and cost of the low-noise amplifiers (LNAs) can be substantial because of the many antennas. We investigate the feasibility of inexpensive, power efficient LNAs, which inherently are less linear. A polynomial model is used to characterize the nonlinear LNAs and to derive the second-order statistics and spatial correlation of the distortion. We show that, with spatial matched filtering (maximum-ratio combining) at the receiver, some distortion terms combine coherently, and that the SINR of the symbol estimates therefore is limited by the linearity of the LNAs. Furthermore, it is studied how the power from a blocker in the adjacent frequency band leaks into the main band and creates distortion. The distortion term that scales cubically with the power received from the blocker has a spatial correlation that can be filtered out by spatial processing and only the coherent term that scales quadratically with the power remains. When the blocker is in free-space line-of-sight and the LNAs are identical, this quadratic term has the same spatial direction as the desired signal, and hence cannot be removed by linear receiver processing

Doctor of Philosophy

dissertationThe demand for high speed communication has been increasing in the past two decades. Multicarrier communication technology has been suggested to address this demand. Orthogonal frequency-division multiplexing (OFDM) is the most widely used multicarrier technique. However, OFDM has a number of disadvantages in time-varying channels, multiple access, and cognitive radios. On the other hand, filterbank multicarrier (FBMC) communication has been suggested as an alternative to OFDM that can overcome the disadvantages of OFDM. In this dissertation, we investigate the application of filtered multitone (FMT), a subset of FBMC modulation methods, to slow fading and fast fading channels. We investigate the FMT transmitter and receiver in continuous and discrete time domains. An efficient implementation of FMT systems is derived and the conditions for perfect reconstruction in an FBMC communication system are presented. We derive equations for FMT in slow fading channels that allow evaluation of FMT when applied to mobile wireless communication systems. We consider using fractionally spaced per tone channel equalizers with different number of taps. The numerical results are presented to investigate the performance of these equalizers. The numerical results show that single-tap equalizers suffice for typical wireless channels. The equalizer design study is advanced by introducing adaptive equalizers which use channel estimation. We derive equations for a minimum mean square error (MMSE) channel estimator and improve the channel estimation by considering the finite duration of channel impulse response. The results of optimum equalizers (when channel is known perfectly) are compared with those of the adaptive equalizers, and it is found that a loss of 1 dB or less incurs. We also introduce a new form of FMT which is specially designed to handle doubly dispersive channels. This method is called FMT-dd (FMT for doubly dispersive channels). The proposed FMT-dd is applied to two common methods of data symbol orientation in the time-frequency space grid; namely, rectangular and hexagonal lattices. The performance of these methods along with OFDM and the conventional FMT are compared and a significant improvement in performance is observed. The FMT-dd design is applied to real-world underwater acoustic (UWA) communication channels. The experimental results from an at-sea experiment (ACOMM10) show that this new design provides a significant gain over OFDM. The feasibility of implementing a MIMO system for multicarrier UWA communication channels is studied through computer simulations. Our study emphasizes the bandwidth efficiency of multicarrier MIMO communications .We show that the value of MIMO to UWA communication is very limited

Estimators of Fractal Dimension: Assessing the Roughness of Time Series and Spatial Data

The fractal or Hausdorff dimension is a measure of roughness (or smoothness) for time series and spatial data. The graph of a smooth, differentiable surface indexed in $\mathbb{R}^d$ has topological and fractal dimension $d$. If the surface is nondifferentiable and rough, the fractal dimension takes values between the topological dimension, $d$, and $d+1$. We review and assess estimators of fractal dimension by their large sample behavior under infill asymptotics, in extensive finite sample simulation studies, and in a data example on arctic sea-ice profiles. For time series or line transect data, box-count, Hall--Wood, semi-periodogram, discrete cosine transform and wavelet estimators are studied along with variation estimators with power indices 2 (variogram) and 1 (madogram), all implemented in the R package fractaldim. Considering both efficiency and robustness, we recommend the use of the madogram estimator, which can be interpreted as a statistically more efficient version of the Hall--Wood estimator. For two-dimensional lattice data, we propose robust transect estimators that use the median of variation estimates along rows and columns. Generally, the link between power variations of index $p>0$ for stochastic processes, and the Hausdorff dimension of their sample paths, appears to be particularly robust and inclusive when $p=1$.Comment: Published in at http://dx.doi.org/10.1214/11-STS370 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Directional edge and texture representations for image processing

An efficient representation for natural images is of fundamental importance in image processing and analysis. The commonly used separable transforms such as wavelets axe not best suited for images due to their inability to exploit directional regularities such as edges and oriented textural patterns; while most of the recently proposed directional schemes cannot represent these two types of features in a unified transform. This thesis focuses on the development of directional representations for images which can capture both edges and textures in a multiresolution manner. The thesis first considers the problem of extracting linear features with the multiresolution Fourier transform (MFT). Based on a previous MFT-based linear feature model, the work extends the extraction method into the situation when the image is corrupted by noise. The problem is tackled by the combination of a "Signal+Noise" frequency model, a refinement stage and a robust classification scheme. As a result, the MFT is able to perform linear feature analysis on noisy images on which previous methods failed. A new set of transforms called the multiscale polar cosine transforms (MPCT) are also proposed in order to represent textures. The MPCT can be regarded as real-valued MFT with similar basis functions of oriented sinusoids. It is shown that the transform can represent textural patches more efficiently than the conventional Fourier basis. With a directional best cosine basis, the MPCT packet (MPCPT) is shown to be an efficient representation for edges and textures, despite its high computational burden. The problem of representing edges and textures in a fixed transform with less complexity is then considered. This is achieved by applying a Gaussian frequency filter, which matches the disperson of the magnitude spectrum, on the local MFT coefficients. This is particularly effective in denoising natural images, due to its ability to preserve both types of feature. Further improvements can be made by employing the information given by the linear feature extraction process in the filter's configuration. The denoising results compare favourably against other state-of-the-art directional representations