Compressive Sensing With Side Information: How to Optimally Capture This Extra Information for GMM Signals?

This paper studies how to optimally capture side information to aid in the reconstruction of high-dimensional signals from low-dimensional random linear and noisy measurements, by assuming that both the signal of interest and the side information signal are drawn from a joint Gaussian mixture model. In particular, we derive sufficient and (occasionally) necessary conditions on the number of linear measurements for the signal reconstruction minimum mean squared error (MMSE) to approach zero in the low-noise regime; moreover, we also derive closed-form linear side information measurement designs for the reconstruction MMSE to approach zero in the low-noise regime. Our designs suggest that a linear projection kernel that optimally captures side information is such that it measures the attributes of side information that are maximally correlated with the signal of interest. A number of experiments both with synthetic and real data confirm that our theoretical results are well aligned with numerical ones. Finally, we offer a case study associated with a panchromatic sharpening (pan sharpening) application in the presence of compressive hyperspectral data that demonstrates that our proposed linear side information measurement designs can lead to reconstruction peak signal-to-noise ratio (PSNR) gains in excess of 2 dB over other approaches in this practical application

Compressive Sensing with Side Information: Analysis, Measurements Design and Applications

Compressive sensing is a breakthrough technology in view of the fact that it enables the acquisition and reconstruction of certain signals with a number of measurements much lower than those dictated by the Shannon-Nyquist paradigm. It has also been recognised in the last few years that it is possible to improve compressive sensing systems by leveraging additional knowledge – so-called side information – that may be available about the signal of interest. The goal of this thesis is to investigate how to improve the acquisition and reconstruction process in compressive sensing systems in the presence of side information. In particular, by assuming that both the signal of interest and the side information obey a joint Gaussian mixture model (GMM), the thesis focuses on the analysis and the design of linear measurements for two different scenarios: i) the scenario where one wishes to design a linear projection matrix to capture the signal of interest; and ii) the scenario where one wishes to design a linear projection matrix to capture the side information. In both cases, we derive sufficient and (occasionally) necessary conditions on the number of measurements needed for the reliable reconstruction in the low-noise regime and we also derive linear measurement designs that are close to optimal. Numerical results are presented with synthetic data from both Gaussian and GMM distributions and with real world imaging data that confirm that analysis is well aligned with practice. We also showcase our measurement design scheme can lead to significant improvement on the application example associated with the reconstruction of high-resolution RGB images from gray scale images using low-resolution, compressive, hyperspectral measurements as side information

Bayesian fusion of multi-band images : A powerful tool for super-resolution

Hyperspectral (HS) imaging, which consists of acquiring a same scene in several hundreds of contiguous spectral bands (a three dimensional data cube), has opened a new range of relevant applications, such as target detection [MS02], classification [C.-03] and spectral unmixing [BDPD+12]. However, while HS sensors provide abundant spectral information, their spatial resolution is generally more limited. Thus, fusing the HS image with other highly resolved images of the same scene, such as multispectral (MS) or panchromatic (PAN) images is an interesting problem. The problem of fusing a high spectral and low spatial resolution image with an auxiliary image of higher spatial but lower spectral resolution, also known as multi-resolution image fusion, has been explored for many years [AMV+11]. From an application point of view, this problem is also important as motivated by recent national programs, e.g., the Japanese next-generation space-borne hyperspectral image suite (HISUI), which fuses co-registered MS and HS images acquired over the same scene under the same conditions [YI13]. Bayesian fusion allows for an intuitive interpretation of the fusion process via the posterior distribution. Since the fusion problem is usually ill-posed, the Bayesian methodology offers a convenient way to regularize the problem by defining appropriate prior distribution for the scene of interest. The aim of this thesis is to study new multi-band image fusion algorithms to enhance the resolution of hyperspectral image. In the first chapter, a hierarchical Bayesian framework is proposed for multi-band image fusion by incorporating forward model, statistical assumptions and Gaussian prior for the target image to be restored. To derive Bayesian estimators associated with the resulting posterior distribution, two algorithms based on Monte Carlo sampling and optimization strategy have been developed. In the second chapter, a sparse regularization using dictionaries learned from the observed images is introduced as an alternative of the naive Gaussian prior proposed in Chapter 1. instead of Gaussian prior is introduced to regularize the ill-posed problem. Identifying the supports jointly with the dictionaries circumvented the difficulty inherent to sparse coding. To minimize the target function, an alternate optimization algorithm has been designed, which accelerates the fusion process magnificently comparing with the simulation-based method. In the third chapter, by exploiting intrinsic properties of the blurring and downsampling matrices, a much more efficient fusion method is proposed thanks to a closed-form solution for the Sylvester matrix equation associated with maximizing the likelihood. The proposed solution can be embedded into an alternating direction method of multipliers or a block coordinate descent method to incorporate different priors or hyper-priors for the fusion problem, allowing for Bayesian estimators. In the last chapter, a joint multi-band image fusion and unmixing scheme is proposed by combining the well admitted linear spectral mixture model and the forward model. The joint fusion and unmixing problem is solved in an alternating optimization framework, mainly consisting of solving a Sylvester equation and projecting onto a simplex resulting from the non-negativity and sum-to-one constraints. The simulation results conducted on synthetic and semi-synthetic images illustrate the advantages of the developed Bayesian estimators, both qualitatively and quantitatively

Multimodal Data Fusion: An Overview of Methods, Challenges and Prospects

International audienceIn various disciplines, information about the same phenomenon can be acquired from different types of detectors, at different conditions, in multiple experiments or subjects, among others. We use the term "modality" for each such acquisition framework. Due to the rich characteristics of natural phenomena, it is rare that a single modality provides complete knowledge of the phenomenon of interest. The increasing availability of several modalities reporting on the same system introduces new degrees of freedom, which raise questions beyond those related to exploiting each modality separately. As we argue, many of these questions, or "challenges" , are common to multiple domains. This paper deals with two key questions: "why we need data fusion" and "how we perform it". The first question is motivated by numerous examples in science and technology, followed by a mathematical framework that showcases some of the benefits that data fusion provides. In order to address the second question, "diversity" is introduced as a key concept, and a number of data-driven solutions based on matrix and tensor decompositions are discussed, emphasizing how they account for diversity across the datasets. The aim of this paper is to provide the reader, regardless of his or her community of origin, with a taste of the vastness of the field, the prospects and opportunities that it holds

‘Open Purpose’: Embracing organizations as expressive systems

The concept of purpose gained prominence in organization theory in recent years but there are discrepant views of its meaning, which we review as evolving and different perspectives: economic theories of the firm; stakeholder approaches; integrative social contracts; and social mission. We elaborate these perspectives in terms of the ebb and flow of ideas and eras. Against these instrumental views, we revisit the work of Robert Cooper, namely the ever-open purpose of expressive organizations, and contrast this with fixist views of purpose in instrumental organizations. We engage with the logic of open purpose and sketch a way of rethinking purpose as a general orientation that constantly evolves and changes over time in interaction with its ecosystem.info:eu-repo/semantics/publishedVersio

Mathematical Approaches for Image Enhancement Problems

This thesis develops novel techniques that can solve some image enhancement problems using theoretically and technically proven and very useful mathematical tools to image processing such as wavelet transforms, partial differential equations, and variational models. Three subtopics are mainly covered. First, color image denoising framework is introduced to achieve high quality denoising results by considering correlations between color components while existing denoising approaches can be plugged in flexibly. Second, a new and efficient framework for image contrast and color enhancement in the compressed wavelet domain is proposed. The proposed approach is capable of enhancing both global and local contrast and brightness as well as preserving color consistency. The framework does not require inverse transform for image enhancement since linear scale factors are directly applied to both scaling and wavelet coefficients in the compressed domain, which results in high computational efficiency. Also contaminated noise in the image can be efficiently reduced by introducing wavelet shrinkage terms adaptively in different scales. The proposed method is able to enhance a wavelet-coded image computationally efficiently with high image quality and less noise or other artifact. The experimental results show that the proposed method produces encouraging results both visually and numerically compared to some existing approaches. Finally, image inpainting problem is discussed. Literature review, psychological analysis, and challenges on image inpainting problem and related topics are described. An inpainting algorithm using energy minimization and texture mapping is proposed. Mumford-Shah energy minimization model detects and preserves edges in the inpainting domain by detecting both the main structure and the detailed edges. This approach utilizes faster hierarchical level set method and guarantees convergence independent of initial conditions. The estimated segmentation results in the inpainting domain are stored in segmentation map, which is referred by a texture mapping algorithm for filling textured regions. We also propose an inpainting algorithm using wavelet transform that can expect better global structure estimation of the unknown region in addition to shape and texture properties since wavelet transforms have been used for various image analysis problems due to its nice multi-resolution properties and decoupling characteristics

Complementarity and the practice of psychotherapy : an alternative to eclecticism

Bibliography: pages 135-149.The problem of competing theories in psychology presents major difficulties for the practicing psychotherapist. These difficulties have traditionally been addressed in mono-theoretical, eclectic and integrative approaches. This work critically examines the problems associated with these traditional methods. It draws on the philosophy of complementarity as postulated by Niels Bohr in order to develop an alternative approach. This philosophy stresses the indeterminate nature of the object of study in psychology, and therefore holds that it is necessary to entertain multiple perspectives. It also holds that in order to counteract the problem of indeterminism there is a need for clarity of theoretical descriptions. For psychotherapy practice this implies, in contradistinction to eclecticism, the separate rather than mixed use of diverse approaches. The practical options suggested by therapeutic complementarity are outlined and their benefits are discussed

Comparison of the vocabularies of the Gregg shorthand dictionary and Horn-Peterson's basic vocabulary of business letters

This study is a comparative analysis of the vocabularies of Horn and Peterson's The Basic Vocabulary of Business Letters1 and the Gregg Shorthand Dictionary.2 Both books purport to present a list of words most frequently encountered by stenographers and students of shorthand. The, Basic Vocabulary of Business Letters, published "in answer to repeated requests for data on the words appearing most frequently in business letters,"3 is a frequency list specific to business writing. Although the book carries the copyright date of 1943, the vocabulary was compiled much earlier. The listings constitute a part of the data used in the preparation of the 10,000 words making up the ranked frequency list compiled by Ernest Horn and staff and published in 1926 under the title of A Basic Writing Vocabulary: 10,000 Words Lost Commonly Used in Writing. The introduction to that publication gives credit to Miss Cora Crowder for the contribution of her Master's study at the University of Minnesota concerning words found in business writing. With additional data from supplementary sources, the complete listing represents twenty-six classes of business, as follows 1. Miscellaneous 2. Florists 3. Automobile manufacturers and sales companie