Computational Imaging and its Application

Traditional optical imaging systems have constrained angular and spatial resolution, depth of field, field of view, tolerance to aberrations and environmental conditions, and other image quality limitations. Computational imaging provided an opportunity to create new functionality and improve the performance of imaging systems by encoding the information optically and decoding it computationally. The design of a computational imaging system balances hardware costs and the accuracy and complexity of the algorithms. In this thesis, two computational imaging systems are presented: Randomized Aperture Imaging and Laser Suppression Imaging. The former system increases the angular resolution of telescopes by replacing a continuous primary mirror with an array of light-weight small mirror elements, which potentially allows telescopes to have very large diameter at a reduced cost. The latter imaging system protects camera sensors from laser effects such as dazzle by use of a phase coded pupil plane mask. Machine learning and deep learning based algorithms were investigated to restore high-fidelity images from the coded acquisitions. The proposed imaging systems are verified by experiment and numerical modeling, and improved performances are demonstrated in comparison with the state-of-the-art

Superresolution imaging of human cytomegalovirus vMIA localization in sub-mitochondrial compartments

The human cytomegalovirus (HCMV) viral mitochondria-localized inhibitor of apoptosis (vMIA) protein, traffics to mitochondria-associated membranes (MAM), where the endoplasmic reticulum (ER) contacts the outer mitochondrial membrane (OMM). vMIA association with the MAM has not been visualized by imaging. Here, we have visualized this by using a combination of confocal and superresolution imaging. Deconvolution of confocal microscopy images shows vMIA localizes away from mitochondrial matrix at the Mitochondria-ER interface. By gated stimulated emission depletion (GSTED) imaging, we show that along this interface vMIA is distributed in clusters. Through multicolor, multifocal structured illumination microscopy (MSIM), we find vMIA clusters localize away from MitoTracker Red, indicating its OMM localization. GSTED and MSIM imaging show vMIA exists in clusters of ~100–150 nm, which is consistent with the cluster size determined by Photoactivated Localization Microscopy (PALM). With these diverse superresolution approaches, we have imaged the clustered distribution of vMIA at the OMM adjacent to the ER. Our findings directly compare the relative advantages of each of these superresolution imaging modalities for imaging components of the MAM and sub-mitochondrial compartments. These studies establish the ability of superresolution imaging to provide valuable insight into viral protein location, particularly in the sub-mitochondrial compartments, and into their clustered organization

Optical Coherence Tomography and Its Non-medical Applications

Optical coherence tomography (OCT) is a promising non-invasive non-contact 3D imaging technique that can be used to evaluate and inspect material surfaces, multilayer polymer films, fiber coils, and coatings. OCT can be used for the examination of cultural heritage objects and 3D imaging of microstructures. With subsurface 3D fingerprint imaging capability, OCT could be a valuable tool for enhancing security in biometric applications. OCT can also be used for the evaluation of fastener flushness for improving aerodynamic performance of high-speed aircraft. More and more OCT non-medical applications are emerging. In this book, we present some recent advancements in OCT technology and non-medical applications

Condensed Mitotic Chromosome Structure at Nanometer Resolution Using PALM and EGFP- Histones

Photoactivated localization microscopy (PALM) and related fluorescent biological imaging methods are capable of providing very high spatial resolutions (up to 20 nm). Two major demands limit its widespread use on biological samples: requirements for photoactivatable/photoconvertible fluorescent molecules, which are sometimes difficult to incorporate, and high background signals from autofluorescence or fluorophores in adjacent focal planes in three-dimensional imaging which reduces PALM resolution significantly. We present here a high-resolution PALM method utilizing conventional EGFP as the photoconvertible fluorophore, improved algorithms to deal with high levels of biological background noise, and apply this to imaging higher order chromatin structure. We found that the emission wavelength of EGFP is efficiently converted from green to red when exposed to blue light in the presence of reduced riboflavin. The photon yield of red-converted EGFP using riboflavin is comparable to other bright photoconvertible fluorescent proteins that allow <20 nm resolution. We further found that image pre-processing using a combination of denoising and deconvolution of the raw PALM images substantially improved the spatial resolution of the reconstruction from noisy images. Performing PALM on Drosophila mitotic chromosomes labeled with H2AvD-EGFP, a histone H2A variant, revealed filamentous components of ∼70 nm. This is the first observation of fine chromatin filaments specific for one histone variant at a resolution approximating that of conventional electron microscope images (10–30 nm). As demonstrated by modeling and experiments on a challenging specimen, the techniques described here facilitate super-resolution fluorescent imaging with common biological samples

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Multi-kernel unmixing and super-resolution using the Modified Matrix Pencil method

Consider L groups of point sources or spike trains, with the l'th group represented by $x_l (t)$. For a function $g : R → R$, let $g_l (t) = g(t/µ_l)$ denote a point spread function with scale $µ_l > 0$, and with $µ_1 < · · · < µ_L$. With $y(t) = \sum_{l=1}^{L} (g_l * x_l)(t)$, our goal is to recover the source parameters given samples of y, or given the Fourier samples of y. This problem is a generalization of the usual super-resolution setup wherein $L = 1$; we call this the multi-kernel unmixing super-resolution problem. Assuming access to Fourier samples of y, we derive an algorithm for this problem for estimating the source parameters of each group, along with precise non-asymptotic guarantees. Our approach involves estimating the group parameters sequentially in the order of increasing scale parameters, i.e., from group 1 to L. In particular, the estimation process at stage $1 ≤ l ≤ L$ involves (i) carefully sampling the tail of the Fourier transform of y, (ii) a deflation step wherein we subtract the contribution of the groups processed thus far from the obtained Fourier samples, and (iii) applying Moitra's modified Matrix Pencil method on a deconvolved version of the samples in (ii)