Data-Driven Image Restoration

Every day many images are taken by digital cameras, and people are demanding visually accurate and pleasing result. Noise and blur degrade images captured by modern cameras, and high-level vision tasks (such as segmentation, recognition, and tracking) require high-quality images. Therefore, image restoration specifically, image deblurring and image denoising is a critical preprocessing step. A fundamental problem in image deblurring is to recover reliably distinct spatial frequencies that have been suppressed by the blur kernel. Existing image deblurring techniques often rely on generic image priors that only help recover part of the frequency spectrum, such as the frequencies near the high-end. To this end, we pose the following specific questions: (i) Does class-specific information offer an advantage over existing generic priors for image quality restoration? (ii) If a class-specific prior exists, how should it be encoded into a deblurring framework to recover attenuated image frequencies? Throughout this work, we devise a class-specific prior based on the band-pass filter responses and incorporate it into a deblurring strategy. Specifically, we show that the subspace of band-pass filtered images and their intensity distributions serve as useful priors for recovering image frequencies. Next, we present a novel image denoising algorithm that uses external, category specific image database. In contrast to existing noisy image restoration algorithms, our method selects clean image “support patches” similar to the noisy patch from an external database. We employ a content adaptive distribution model for each patch where we derive the parameters of the distribution from the support patches. Our objective function composed of a Gaussian fidelity term that imposes category specific information, and a low-rank term that encourages the similarity between the noisy and the support patches in a robust manner. Finally, we propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules (CIMM) for image denoising. The CIMM structure possesses two distinctive features that are important for the noise removal task. Firstly, each residual unit employs identity mappings as the skip connections and receives pre-activated input to preserve the gradient magnitude propagated in both the forward and backward directions. Secondly, by utilizing dilated kernels for the convolution layers in the residual branch, each neuron in the last convolution layer of each module can observe the full receptive field of the first layer

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

Restaurierung von kohärenten Bildern

In this thesis a series of novel algorithms for high quality restoration of coherent images is introduced. This task cannot be solved with established methods for the restoration of incoherent images. These algorithms focus on the correction of images in coherent imaging systems with a-priori known aberrations. The new wavefront correction algorithms achieve a significantly higher restoration quality than any previously known technique. The algorithms in this thesis are based on latest advances in optimization algorithms, particularly projections onto convex sets, proximal optimization and fractal self-similarity. Convergence and performance of the individual algorithms are analyzed in detail in various scenarios on real and simulated images. The evaluation also deals with the impact of noise on the restoration quality. Practical application of the new algorithms on microscopic images of diverse biological and human samples, as well as shadowgraph images of plankton acquired with a laboratory setup prove their efficiency. The new algorithms also have promising future applications in other areas, for example in adaptive optics and astronomy.In dieser Thesis werden mehrere neue Algorithmen für eine qualitativ hochwertige Restaurierung von kohärenten Bildern vorgestellt. Diese Aufgabe kann mit den bekannten Methoden für die Restaurierung von nicht kohärenten Bildern nicht gelöst werden. Die neuen Algorithmen sind auf die Wiederherstellung von Bildern in kohärenten Abbildungssystemen, bei denen die Aberrationen a-priori bekannt sind, ausgerichtet. Sie dienen der Korrektur der Wellenfront und erreichen eine wesentlich höhere Qualität der Bildrekonstruktion als sämtliche vorbekannte Verfahren. Die Algorithmen in dieser Thesis basieren auf neuesten Optimierungsalgorithmen, wie Projektionen in konvexe Sets, proximale Optimierung und fraktaler Ähnlichkeit. Die Konvergenz und Leistung der einzelnen Algorithmen wird ausführlich in unterschiedlichen Szenarien mit simulierten und realen Bildern untersucht. Eine praktische Erprobung der neuen Algorithmen an mikroskopischen Aufnahmen von unterschiedlichen biologischen und humanen Proben, wie auch an Aufnahmen vom Shadowgraph, bestätigt ihre Effizienz. Die neuen Algorithmen haben vielversprechende künftige Anwendungen, auch in anderen Gebieten, z.B. in der adaptiven Optik und der Astronomie

PET/MR imaging of hypoxic atherosclerotic plaque using 64Cu-ATSM

ABSTRACT OF THE DISSERTATION PET/MR Imaging of Hypoxic Atherosclerotic Plaque Using 64Cu-ATSM by Xingyu Nie Doctor of Philosophy in Biomedical Engineering Washington University in St. Louis, 2017 Professor Pamela K. Woodard, Chair Professor Suzanne Lapi, Co-Chair It is important to accurately identify the factors involved in the progression of atherosclerosis because advanced atherosclerotic lesions are prone to rupture, leading to disability or death. Hypoxic areas have been known to be present in human atherosclerotic lesions, and lesion progression is associated with the formation of lipid-loaded macrophages and increased local inflammation which are potential major factors in the formation of vulnerable plaque. This dissertation work represents a comprehensive investigation of non-invasive identification of hypoxic atherosclerotic plaque in animal models and human subjects using the PET hypoxia imaging agent 64Cu-ATSM. We first demonstrated the feasibility of 64Cu-ATSM for the identification of hypoxic atherosclerotic plaque and evaluated the relative effects of diet and genetics on hypoxia progression in atherosclerotic plaque in a genetically-altered mouse model. We then fully validated the feasibility of using 64Cu-ATSM to image the extent of hypoxia in a rabbit model with atherosclerotic-like plaque using a simultaneous PET-MR system. We also proceeded with a pilot clinical trial to determine whether 64Cu-ATSM MR/PET scanning is capable of detecting hypoxic carotid atherosclerosis in human subjects. In order to improve the 64Cu-ATSM PET image quality, we investigated the Siemens HD (high-definition) PET software and 4 partial volume correction methods to correct for partial volume effects. In addition, we incorporated the attenuation effect of the carotid surface coil into the MR attenuation correction _-map to correct for photon attention. In the long term, this imaging strategy has the potential to help identify patients at risk for cardiovascular events, guide therapy, and add to the understanding of plaque biology in human patients