Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

Photoacoustic Imaging, Feature Extraction, and Machine Learning Implementation for Ovarian and Colorectal Cancer Diagnosis

Among all cancers related to women’s reproductive systems, ovarian cancer has the highest mortality rate. Pelvic examination, transvaginal ultrasound (TVUS), and blood testing for cancer antigen 125 (CA-125), are the conventional screening tools for ovarian cancer, but they offer very low specificity. Other tools, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), also have limitations in detecting small lesions. In the USA, considering men and women separately, colorectal cancer is the third most common cause of death related to cancer; for men and women combined, it is the second leading cause of cancer deaths. It is estimated that in 2021, 52,980 deaths due to this cancer will be recorded. The common screening tools for colorectal cancer diagnosis include colonoscopy, biopsy, endoscopic ultrasound (EUS), optical imaging, pelvic MRI, CT, and PET, which all have specific limitations. In this dissertation, we first discuss in-vivo ovarian cancer diagnosis using our coregistered photoacoustic tomography and ultrasound (PAT/US) system. The application of this system is also explored in colorectal cancer diagnosis ex-vivo. Finally, we discuss the capability of our photoacoustic microscopy (PAM) system, complemented by machine learning algorithms, in distinguishing cancerous rectums from normal ones. The dissertation starts with discussing our low-cost phantom construction procedure for pre-clinical experiments and quantitative PAT. This phantom has ultrasound and photoacoustic properties similar to those of human tissue, making it a good candidate for photoacoustic imaging experiments. In-vivo ovarian cancer diagnosis using our PAT/US system is then discussed. We demonstrate extraction of spectral, image, and functional features from our PAT data. These features are then used to distinguish malignant (n=12) from benign ovaries (n=27). An AUC of 0.93 is achieved using our developed SVM classifier. We then explain a sliding multi-pixel method to mitigate the effect of noise on the estimation of functional features from PAT data. This method is tested on 13 malignant and 36 benign ovaries. After that, we demonstrate our two-step optimization method for unmixing the optical absorption (μa) of the tissue from the system response (C) and Grüneisen parameter (Γ) in quantitative PAT (QPAT). Using this method, we calculate the absorption coefficient and functional parameters of five blood tubes, with sO2 values ranging from 24.9% to 97.6%. We then demonstrate the capability of our PAT/US system in monitoring colorectal cancer treatment as well as classifying 13 malignant and 17 normal colon samples. Using PAT features to distinguish these two types of samples (malignant and normal colons), our classifier can achieve an AUC of 0.93. After that, we demonstrate the capability of our coregistered photoacoustic microscopy and ultrasound (PAM/US) system in distinguishing normal from malignant colorectal tissue. It is shown that a convolutional neural network (CNN) significantly outperforms the generalized regression model (GLM) in distinguishing these two types of lesions

Robust and Efficient Deep Visual Learning

The past decade was marked by significant progress in the field of artificial intelligence and statistical learning. However, the most impressive of modern models come in the form of computationally expensive black boxes, with the majority of them lacking the ability to reason about the confidence of their predictions robustly. Being capable of quantifying model uncertainty and recognizing failure scenarios is crucial when it comes to incorporating them into complex decision-making pipelines, e.g. autonomous driving or medical image analysis systems. It is also important to maintain a low computational cost of these models. In the present thesis, the aforementioned desired properties of robustness and efficiency of deep learning models are studied and developed in the three specific realms of computer vision. First, we investigate deep probabilistic models that allow uncertainty quantification, i.e. the models that "know what they do not know". Here, we propose a novel model for the task of angular regression that allows probabilistic object pose estimation from 2D images. We also showcase how the general deep density estimation paradigm can be adapted and utilized in two other real-world applications, ball trajectory prediction and brain imaging. Next, we turn to the field of 3D shape analysis and rendering. We propose a method for efficient encoding of 3D point clouds, the type of data that is hard to handle with conventional learning algorithms due to its unordered nature. We show that simple neural networks that use the developed encoding as input can match the performance of state-of-the-art methods on various point cloud processing tasks while using orders of magnitude less floating-point operations. Finally, we explore the emerging field of neural rendering and develop the framework that connects classic deformable 3D body models with modern image-to-image translation neural networks. This combination allows efficient photorealistic human avatar rendering in a controlled manner, with the possibility to control the camera flexibly and to change the body pose and shape appearance. The thesis concludes with the discussion of the presented methods, including current limitations and future research directions

Dimension-reduction and discrimination of neuronal multi-channel signals

Dimensionsreduktion und Trennung neuronaler Multikanal-Signale

OCM 2023 - Optical Characterization of Materials : Conference Proceedings

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

Optical Techniques for Fruit Firmness Assessment

This thesis describes the design and development of a new high-speed multispectral imaging (MSI) system compatible with a commercial grading line. The purpose of this system was to carry out spatially resolved spectroscopy to assess fruit firmness. Captured images were analysed using diffusion theory and modified Lorentzian models to extract a sample’s optical properties (absorption and reduced scattering coefficients) and optical parameters respectively. The high-speed MSI system was designed to capture images of fruit using a high-resolution complementary metal–oxide–semiconductor camera, 12.5 mm lens, and discrete lasers operating at 685, 850, and 904 nm. Each laser illuminates a separate fruit, and the camera captures the interacting light with a single frame encompassing all three fruit. Depending on the size of each fruit the spatial resolution with the 12.5 mm lens ranged from 0.15 to 0.22 mm/pixel. Initial measurements were made on 200 ‘Royal Gala’ apples to identify the relationships between the optical properties or parameters and either acoustic or the industry standard penetrometer firmness measurements. Performance of the high speed MSI system was poor compared to the results seen in the literature using alternative spatially resolved spectroscopic systems and other apple varieties. Only weak correlations (R = 0.33) were found between the individual optical measurements and firmness. Unsatisfactory performance from the high-speed system led to the development of a static MSI system to measure stationary fruit and the development of an inverse adding-doubling (IAD) system to provide an independent measurement of the samples optical properties. The purpose of these systems was to help understand the measurement, reduce variability, and give an indication of the upper level of performance possible. The static MSI system featured a number of improvements including the addition of a 980 nm laser, the elimination of an asymmetry caused by laser polarisation, improved temperature control, an electronic shutter system, precise location control of the fruit, and a new 25 mm lens improving spatial resolution (0.057mm/pixel). A second study was carried out using the new MSI and IAD systems on 92 ‘Royal Gala’ apples. Fruit were sliced to expose a flat measurement surface eliminating variation caused by fruit curvature and skin pigments. With these refinements and simplifications the relationships between optical properties or parameters and penetrometer firmness strengthened. As fruit softened and penetrometer firmness fell the reduced scattering coefficient measured by both the IAD and MSI system increased with correlation coefficients ranging from -0.62 to -0.70. The absorption coefficients measured by the two systems showed the expected features related to the absorption of chlorophyll and carotenoid pigments, and water absorption. As the fruit softened chlorophyll absorption decreased as the pigments are broken down and carotenoid absorption increased as new pigments are synthesised. No useful relationships were identified between the optical measurements and acoustic firmness. Multiple linear regression models were formed to predict penetrometer firmness using either the optical properties or modified Lorentzian parameters. The best performing model used a combination of the absorption and scattering coefficients, and had a correlation coefficient of 0.8 and a standard error of 5.87 N

Bayesian Field Theory: Nonparametric Approaches to Density Estimation, Regression, Classification, and Inverse Quantum Problems

Bayesian field theory denotes a nonparametric Bayesian approach for learning functions from observational data. Based on the principles of Bayesian statistics, a particular Bayesian field theory is defined by combining two models: a likelihood model, providing a probabilistic description of the measurement process, and a prior model, providing the information necessary to generalize from training to non-training data. The particular likelihood models discussed in the paper are those of general density estimation, Gaussian regression, clustering, classification, and models specific for inverse quantum problems. Besides problem typical hard constraints, like normalization and positivity for probabilities, prior models have to implement all the specific, and often vague, "a priori" knowledge available for a specific task. Nonparametric prior models discussed in the paper are Gaussian processes, mixtures of Gaussian processes, and non-quadratic potentials. Prior models are made flexible by including hyperparameters. In particular, the adaption of mean functions and covariance operators of Gaussian process components is discussed in detail. Even if constructed using Gaussian process building blocks, Bayesian field theories are typically non-Gaussian and have thus to be solved numerically. According to increasing computational resources the class of non-Gaussian Bayesian field theories of practical interest which are numerically feasible is steadily growing. Models which turn out to be computationally too demanding can serve as starting point to construct easier to solve parametric approaches, using for example variational techniques.Comment: 200 pages, 99 figures, LateX; revised versio

AFIT School of Engineering Contributions to Air Force Research and Technology Calendar Year 1973

This report contains abstracts of Master of Science Theses, Doctoral dissertations, and selected faculty publications completed during the 1973 calendar year at the School of Engineering, Air Force Institute of Technology, at Wright-Patterson Air Force Base, Ohio

AFIT School of Engineering Contributions to Air Force Research and Technology Calendar Year 1973

This report contains abstracts of Master of Science Theses, Doctoral dissertations, and selected faculty publications completed during the 1973 calendar year at the School of Engineering, Air Force Institute of Technology, at Wright-Patterson Air Force Base, Ohio

Mathematical Modeling and Simulation in Mechanics and Dynamic Systems

The present book contains the 16 papers accepted and published in the Special Issue “Mathematical Modeling and Simulation in Mechanics and Dynamic Systems” of the MDPI “Mathematics” journal, which cover a wide range of topics connected to the theory and applications of Modeling and Simulation of Dynamic Systems in different field. These topics include, among others, methods to model and simulate mechanical system in real engineering. It is hopped that the book will find interest and be useful for those working in the area of Modeling and Simulation of the Dynamic Systems, as well as for those with the proper mathematical background and willing to become familiar with recent advances in Dynamic Systems, which has nowadays entered almost all sectors of human life and activity