Probabilistic modeling of wavelet coefficients for processing of image and video signals

Statistical estimation and detection techniques are widely used in signal processing including wavelet-based image and video processing. The probability density function (PDF) of the wavelet coefficients of image and video signals plays a key role in the development of techniques for such a processing. Due to the fixed number of parameters, the conventional PDFs for the estimators and detectors usually ignore higher-order moments. Consequently, estimators and detectors designed using such PDFs do not provide a satisfactory performance. This thesis is concerned with first developing a probabilistic model that is capable of incorporating an appropriate number of parameters that depend on higher-order moments of the wavelet coefficients. This model is then used as the prior to propose certain estimation and detection techniques for denoising and watermarking of image and video signals. Towards developing the probabilistic model, the Gauss-Hermite series expansion is chosen, since the wavelet coefficients have non-compact support and their empirical density function shows a resemblance to the standard Gaussian function. A modification is introduced in the series expansion so that only a finite number of terms can be used for modeling the wavelet coefficients with rendering the resulting PDF to become negative. The parameters of the resulting PDF, called the modified Gauss-Hermite (NIGH) PDF, are evaluated in terms of the higher-order sample-moments. It is shown that the MGH PDF fits the empirical density function better than the existing PDFs that use a limited number of parameters do. The proposed MGH PDF is used as the prior of image and video signals in designing maximum a posteriori and minimum mean squared error-based estimators for denoising of image and video signals and log-likelihood ratio-based detector for watermarking of image signals. The performance of the estimation and detection techniques are then evaluated in terms of the commonly used metrics. It is shown through extensive experimentations that the estimation and detection techniques developed utilizing the proposed MGH PDF perform substantially better than those that utilize the conventional PDFs. These results confirm that the superior fit of the MGH PDF to the empirical density function resulting from the flexibility of the MGH PDF in choosing the number of parameters, which are functions of higher-order moments of data, leads to the better performance. Thus, the proposed MGH PDF should play a significant role in wavelet-based image and video signal processin

Textural Difference Enhancement based on Image Component Analysis

In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method

Textural Difference Enhancement based on Image Component Analysis

In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method

Design and Analysis of A New Illumination Invariant Human Face Recognition System

In this dissertation we propose the design and analysis of a new illumination invariant face recognition system. We show that the multiscale analysis of facial structure and features of face images leads to superior recognition rates for images under varying illumination. We assume that an image I ( x,y ) is a black box consisting of a combination of illumination and reflectance. A new approximation is proposed to enhance the illumination removal phase. As illumination resides in the low-frequency part of images, a high-performance multiresolution transformation is employed to accurately separate the frequency contents of input images. The procedure is followed by a fine-tuning process. After extracting a mask, feature vector is formed and the principal component analysis (PCA) is used for dimensionality reduction which is then proceeded by the extreme learning machine (ELM) as a classifier. We then analyze the effect of the frequency selectivity of subbands of the transformation on the performance of the proposed face recognition system. In fact, we first propose a method to tune the characteristics of a multiresolution transformation, and then analyze how these specifications may affect the recognition rate. In addition, we show that the proposed face recognition system can be further improved in terms of the computational time and accuracy. The motivation for this progress is related to the fact that although illumination mostly lies in the low-frequency part of images, these low-frequency components may have low- or high-resonance nature. Therefore, for the first time, we introduce the resonance based analysis of face images rather than the traditional frequency domain approaches. We found that energy selectivity of the subbands of the resonance based decomposition can lead to superior results with less computational complexity. The method is free of any prior information about the face shape. It is systematic and can be applied separately on each image. Several experiments are performed employing the well known databases such as the Yale B, Extended-Yale B, CMU-PIE, FERET, AT&T, and LFW. Illustrative examples are given and the results confirm the effectiveness of the method compared to the current results in the literature