Structural adaptive anisotropic recursive filter for blind medical image deconvolution

Performance of radiographic diagnosis and therapeutic intervention heavily depends on the quality of acquired images. Over decades, a range of pre-processing for image enhancement has been explored. Among the most recent proposals is iterative blinded image deconvolution, which aims to identify the inheritant point spread function, degrading images during acquisition. Thus far, the technique has been known for its poor convergence and stability and was recently superseded by non-negativity and support constraints recursive image filtering. However, the latter requires a priori on intrinsic properties of imaging sensor, e.g., distribution, noise floor and field of view. Most importantly, since homogeneity assumption was implied by deconvolution, recovered degrading function was global, disregarding fidelity of underlying objects. This paper proposes a modified recursive filtering with similar non-negativity constraints, but also taking into account local anisotropic structure of content. The experiment reported herein demonstrates its superior convergence property, while also preserving crucial image feature

Restauration de défauts de S-VHS

Le VHS est encore le format de bande magnétique le plus populaire dans le domaine de l'enregistrement de vidéo pour les consommateurs. En raison de la limitation de la résolution horizontale, une version améliorée du format VHS a été introduite: le format S-VHS. Bien que le nouveau format permette de reproduire l'image avec une bonne qualité, il y a encore des défauts: (1) L'image semble brouillée, et les détails manquent de netteté. (2) Les couleurs sont dégradées et manquent d'éclat. Ce travail propose deux méthodes de restauration de défauts de S-VHS. La première combine les techniques de débrouillage avec les techniques de rehaussement. Dans cette méthode, la luminance et la chrominance sont traitées séparément. La deuxième approche proposée utilise une structure de réseaux de neurones en cascade et permet d'utiliser l'information dans la luminance traitée afin de mieux restaurer la chrominance. Les résultats d'expériences ont montré que les deux méthodes proposées sont capables de donner des images plus nettes et plus éclatantes. En plus, grâce à la simplicité et la non-itération, il est possible de les faire fonctionner en temps-reél

Blind image deconvolution: nonstationary Bayesian approaches to restoring blurred photos

High quality digital images have become pervasive in modern scientific and everyday life — in areas from photography to astronomy, CCTV, microscopy, and medical imaging. However there are always limits to the quality of these images due to uncertainty and imprecision in the measurement systems. Modern signal processing methods offer the promise of overcoming some of these problems by postprocessing these blurred and noisy images. In this thesis, novel methods using nonstationary statistical models are developed for the removal of blurs from out of focus and other types of degraded photographic images. The work tackles the fundamental problem blind image deconvolution (BID); its goal is to restore a sharp image from a blurred observation when the blur itself is completely unknown. This is a “doubly illposed” problem — extreme lack of information must be countered by strong prior constraints about sensible types of solution. In this work, the hierarchical Bayesian methodology is used as a robust and versatile framework to impart the required prior knowledge. The thesis is arranged in two parts. In the first part, the BID problem is reviewed, along with techniques and models for its solution. Observation models are developed, with an emphasis on photographic restoration, concluding with a discussion of how these are reduced to the common linear spatially-invariant (LSI) convolutional model. Classical methods for the solution of illposed problems are summarised to provide a foundation for the main theoretical ideas that will be used under the Bayesian framework. This is followed by an indepth review and discussion of the various prior image and blur models appearing in the literature, and then their applications to solving the problem with both Bayesian and nonBayesian techniques. The second part covers novel restoration methods, making use of the theory presented in Part I. Firstly, two new nonstationary image models are presented. The first models local variance in the image, and the second extends this with locally adaptive noncausal autoregressive (AR) texture estimation and local mean components. These models allow for recovery of image details including edges and texture, whilst preserving smooth regions. Most existing methods do not model the boundary conditions correctly for deblurring of natural photographs, and a Chapter is devoted to exploring Bayesian solutions to this topic. Due to the complexity of the models used and the problem itself, there are many challenges which must be overcome for tractable inference. Using the new models, three different inference strategies are investigated: firstly using the Bayesian maximum marginalised a posteriori (MMAP) method with deterministic optimisation; proceeding with the stochastic methods of variational Bayesian (VB) distribution approximation, and simulation of the posterior distribution using the Gibbs sampler. Of these, we find the Gibbs sampler to be the most effective way to deal with a variety of different types of unknown blurs. Along the way, details are given of the numerical strategies developed to give accurate results and to accelerate performance. Finally, the thesis demonstrates state of the art results in blind restoration of synthetic and real degraded images, such as recovering details in out of focus photographs

A longitudinal study of the experiences and psychological well-being of Indian surrogates

Study question: What is the psychological well-being of Indian surrogates during and after the surrogacy pregnancy? Summary answer: Surrogates were similar to a matched group of expectant mothers on anxiety and stress. However, they scored higher on depression during and after pregnancy. What is known already: The recent ban on trans-national commercial surrogacy in India has led to urgent policy discussions regarding surrogacy. Whilst previous studies have reported the motivations and experiences of Indian surrogates no studies have systematically examined the psychological well-being of Indian surrogates, especially from a longitudinal perspective. Previous research has shown that Indian surrogates are motivated by financial payment and may face criticism from their family and community due to negative social stigma attached to surrogacy. Indian surrogates often recruited by agencies and mainly live together in a “surrogacy house.” Study design, size, duration: A longitudinal study was conducted comparing surrogates to a matched group of expectant mothers over two time points: (a) during pregnancy (Phase1: 50 surrogates, 70 expectant mothers) and (b) 4–6 months after delivery (Phase 2: 45 surrogates, 49 expectant mothers). The Surrogates were recruited from a fertility clinic in Mumbai and the matched comparison group was recruited from four public hospitals in Mumbai and Delhi. Data collection was completed over 2 years. Participants/materials, setting, methods: Surrogates and expectant mothers were aged between 23 and 36 years. All participants were from a low socio-economic background and had left school before 12–13 years of age. In-depth faceto-face semi-structured interviews and a psychological questionnaire assessing anxiety, stress and depression were administered in Hindi to both groups. Interviews took place in a private setting. Audio recordings of surrogate interviews were later translated and transcribed into English. Main results and the role of chance: Stress and anxiety levels did not significantly differ between the two groups for both phases of the study. For depression, surrogates were found to be significantly more depressed than expectant mothers at phase 1 (p = 0.012) and phase 2 (p = 0.017). Within the surrogacy group, stress and depression did not change during and after pregnancy. However, a non-significant trend was found showing that anxiety decreased after delivery (p = 0.086). No participants reported being coerced into surrogacy, however nearly all kept it a secret from their wider family and community and hence did not face criticism. Surrogates lived at the surrogate house for different durations. During pregnancy, 66% (N = 33/50) reported their experiences of the surrogate house as positive, 24% (N = 12/50) as negative and 10% (N = 5/50) as neutral. After delivery, most surrogates (66%, N = 30/45) reported their experiences of surrogacy to be positive, with the remainder viewing it as neutral (28%) or negative (4%). In addition, most (66%, N = 30/45) reported that they had felt “socially supported and loved” during the surrogacy arrangement by friends in the surrogate hostel, clinic staff or family. Most surrogates did not meet the intending parents (49%, N = 22/45) or the resultant child (75%, N = 34/45). Limitations, reasons for caution: Since the surrogates were recruited from only one clinic, the findings may not be representative of all Indian surrogates. Some were lost to follow-up which may have produced sampling bias. Wider implications of the findings: This is the first study to examine the psychological well-being of surrogates in India. This research is of relevance to current policy discussions in India regarding legislation on surrogacy. Moreover, the findings are of relevance to clinicians, counselors and other professionals involved in surrogacy. Trial registration number: N/A

Fluorescence Methods for Investigation of Living Cells and Microorganisms

Fluorescence methods play a leading role in the investigation of biological objects. They are the only non-destructive methods for investigating living cells and microorganisms in vivo. Using intrinsic and artificial fluorescence methods provides deep insight into mechanisms underlying physiological and biochemical processes. This book covers a wide range of modern methods involved in experimental biology. It illustrates the use of fluorescence microscopy and spectroscopy, confocal laser scanning microscopy, flow cytometry, delayed fluorescence, pulse-amplitude-modulation fluorometry, and fluorescent dye staining protocols. This book provides an overview of practical and theoretical aspects of fluorescence methods and their successful application in the investigation of static and dynamic processes in living cells and microorganisms