움직이는 단일 카메라를 이용한 3차원 복원과 디블러링, 초해상도 복원의 동시적 수행 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. 이경무.영상 기반 3차원 복원은 컴퓨터 비전의 기본적인 연구 주제 가운데 하나로 최근 몇 년간 많은 발전이 있어왔다. 특히 자동 로봇을 위한 네비게이션 및 휴대 기기를 이용한 증강 현실 등에 널리 활용될 수 있는 단일 카메라를 이용한 3차원 복원 기법은 복원의 정확도, 복원 가능 범위 및 처리 속도 측면에서 많은 실용 가능성을 보여주고 있다. 그러나 그 성능은 여전히 조심스레 촬영된 높은 품질의 입력 영상에 대해서만 시험되고 있다. 움직이는 단일 카메라를 이용한 3차원 복원의 실제 동작 환경에서는 입력 영상이 화소 잡음이나 움직임에 의한 번짐 등에 의하여 손상될 수 있고, 영상의 해상도 또한 정확한 카메라 위치 인식 및 3차원 복원을 위해서는 충분히 높지 않을 수 있다. 많은 연구에서 고성능 영상 화질 향상 기법들이 제안되어 왔지만 이들은 일반적으로 높은 계산 비용을 필요로 하기 때문에 실시간 동작 능력이 중요한 단일 카메라 기반 3차원 복원에 사용되기에는 부적합하다. 본 논문에서는 보다 정확하고 안정된 복원을 위하여 영상 개선이 결합된 새로운 단일 카메라 기반 3차원 복원 기법을 다룬다. 이를 위하여 영상 품질이 저하되는 중요한 두 요인인 움직임에 의한 영상 번짐과 낮은 해상도 문제가 각각 점 기반 복원 및 조밀 복원 기법들과 결합된다. 영상 품질 저하를 포함한 영상 획득 과정은 카메라 및 장면의 3차원 기하 구조와 관측된 영상 사이의 관계를 이용하여 모델링 할 수 있고, 이러한 영상 품질 저하 과정을 고려함으로써 정확한 3차원 복원을 하는 것이 가능해진다. 또한, 영상 번짐 제거를 위한 번짐 커널 또는 영상의 초해상도 복원을 위한 화소 대응 정보 등이 3차원 복원 과정과 동시에 얻어지는것이 가능하여, 영상 개선이 보다 간편하고 빠르게 수행될 수 있다. 제안되는 기법은 3차원 복원과 영상 개선 문제를 동시에 해결함으로써 각각의 결과가 상호 보완적으로 향상된다는 점에서 그 장점을 가지고 있다. 본 논문에서는 실험적 평가를 통하여 제안되는 3차원 복원 및 영상 개선의 효과성을 입증하도록 한다.Vision-based 3D reconstruction is one of the fundamental problems in computer vision, and it has been researched intensively significantly in the last decades. In particular, 3D reconstruction using a single camera, which has a wide range of applications such as autonomous robot navigation and augmented reality, shows great possibilities in its reconstruction accuracy, scale of reconstruction coverage, and computational efficiency. However, until recently, the performances of most algorithms have been tested only with carefully recorded, high quality input sequences. In practical situations, input images for 3D reconstruction can be severely distorted due to various factors such as pixel noise and motion blur, and the resolution of images may not be high enough to achieve accurate camera localization and scene reconstruction results. Although various high-performance image enhancement methods have been proposed in many studies, the high computational costs of those methods prevent applying them to the 3D reconstruction systems where the real-time capability is an important issue. In this dissertation, novel single camera-based 3D reconstruction methods that are combined with image enhancement methods is studied to improve the accuracy and reliability of 3D reconstruction. To this end, two critical image degradations, motion blur and low image resolution, are addressed for both sparse reconstruction and dense 3D reconstruction systems, and novel integrated enhancement methods for those degradations are presented. Using the relationship between the observed images and 3D geometry of the camera and scenes, the image formation process including image degradations is modeled by the camera and scene geometry. Then, by taking the image degradation factors in consideration, accurate 3D reconstruction then is achieved. Furthermore, the information required for image enhancement, such as blur kernels for deblurring and pixel correspondences for super-resolution, is simultaneously obtained while reconstructing 3D scene, and this makes the image enhancement much simpler and faster. The proposed methods have an advantage that the results of 3D reconstruction and image enhancement are improved by each other with the simultaneous solution of these problems. Experimental evaluations demonstrate the effectiveness of the proposed 3D reconstruction and image enhancement methods.1. Introduction 2. Sparse 3D Reconstruction and Image Deblurring 3. Sparse 3D Reconstruction and Image Super-Resolution 4. Dense 3D Reconstruction and Image Deblurring 5. Dense 3D Reconstruction and Image Super-Resolution 6. Dense 3D Reconstruction, Image Deblurring, and Super-Resolution 7. ConclusionDocto

Long Range Automated Persistent Surveillance

This dissertation addresses long range automated persistent surveillance with focus on three topics: sensor planning, size preserving tracking, and high magnification imaging. field of view should be reserved so that camera handoff can be executed successfully before the object of interest becomes unidentifiable or untraceable. We design a sensor planning algorithm that not only maximizes coverage but also ensures uniform and sufficient overlapped camera’s field of view for an optimal handoff success rate. This algorithm works for environments with multiple dynamic targets using different types of cameras. Significantly improved handoff success rates are illustrated via experiments using floor plans of various scales. Size preserving tracking automatically adjusts the camera’s zoom for a consistent view of the object of interest. Target scale estimation is carried out based on the paraperspective projection model which compensates for the center offset and considers system latency and tracking errors. A computationally efficient foreground segmentation strategy, 3D affine shapes, is proposed. The 3D affine shapes feature direct and real-time implementation and improved flexibility in accommodating the target’s 3D motion, including off-plane rotations. The effectiveness of the scale estimation and foreground segmentation algorithms is validated via both offline and real-time tracking of pedestrians at various resolution levels. Face image quality assessment and enhancement compensate for the performance degradations in face recognition rates caused by high system magnifications and long observation distances. A class of adaptive sharpness measures is proposed to evaluate and predict this degradation. A wavelet based enhancement algorithm with automated frame selection is developed and proves efficient by a considerably elevated face recognition rate for severely blurred long range face images

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

Deep learning-based diagnostic system for malignant liver detection

Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent, accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification. In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms. However, such traditional methods could immensely affect the structural properties of processed images with inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use. To address these limitations, I propose novel methodologies in this dissertation. First, I modified a generative adversarial network to perform deblurring and contrast adjustment on computed tomography (CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver detection. The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods. The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification. A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions. Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants. In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore, the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis

Computational Imaging Approach to Recovery of Target Coordinates Using Orbital Sensor Data

This dissertation addresses the components necessary for simulation of an image-based recovery of the position of a target using orbital image sensors. Each component is considered in detail, focusing on the effect that design choices and system parameters have on the accuracy of the position estimate. Changes in sensor resolution, varying amounts of blur, differences in image noise level, selection of algorithms used for each component, and lag introduced by excessive processing time all contribute to the accuracy of the result regarding recovery of target coordinates using orbital sensor data. Using physical targets and sensors in this scenario would be cost-prohibitive in the exploratory setting posed, therefore a simulated target path is generated using Bezier curves which approximate representative paths followed by the targets of interest. Orbital trajectories for the sensors are designed on an elliptical model representative of the motion of physical orbital sensors. Images from each sensor are simulated based on the position and orientation of the sensor, the position of the target, and the imaging parameters selected for the experiment (resolution, noise level, blur level, etc.). Post-processing of the simulated imagery seeks to reduce noise and blur and increase resolution. The only information available for calculating the target position by a fully implemented system are the sensor position and orientation vectors and the images from each sensor. From these data we develop a reliable method of recovering the target position and analyze the impact on near-realtime processing. We also discuss the influence of adjustments to system components on overall capabilities and address the potential system size, weight, and power requirements from realistic implementation approaches

Separable Inverse Problems, Blind Deconvolution, and Stray Light Correction for Extreme Ultraviolet Solar Images.

The determination of the inputs to a system given noisy output data is known as an inverse problem. When the system is a linear transformation involving unknown side parameters, the problem is called separable. A quintessential separable inverse problem is blind deconvolution: given a blurry image one must determine the sharp image and point spread function (PSF) that were convolved together to form it. This thesis describes a novel optimization approach for general separable inverse problems, a new blind deconvolution method for images corrupted by camera shake, and the first stray light correction for extreme ultraviolet (EUV) solar images from the EUVI/STEREO instruments. We present a generalization of variable elimination methods for separable inverse problems beyond least squares. Existing variable elimination methods require an explicit formula for the optimal value of the linear variables, so they cannot be used in problems with Poisson likelihoods, bound constraints, or other important departures from least squares. To address this limitation, we propose a generalization of variable elimination in which standard optimization methods are modified to behave as though a variable has been eliminated. Computational experiments indicate that this approach can have significant speed and robustness advantages. A new incremental sparse approximation method is proposed for blind deconvolution of images corrupted by camera shake. Unlike current state-of-the-art variational Bayes methods, it is based on simple alternating projected gradient optimization. In experiments on a standard test set, our method is faster than the state-of-the-art and competitive in deblurring performance. Stray light PSFs are determined for the two EUVI instruments, EUVI-A and B, aboard the STEREO mission. The PSFs are modeled using semi-empirical parametric formulas, and their parameters are determined by semiblind deconvolution of EUVI images. The EUVI-B PSFs were determined from lunar transit data, exploiting the fact that the Moon is not a significant EUV source. The EUVI-A PSFs were determined by analysis of simultaneous A/B observations from December 2006, when the instruments had nearly identical lines of sight to the Sun. We provide the first estimates of systematic error in EUV deconvolved images.PHDApplied and Interdisciplinary MathematicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99797/1/shearerp_1.pd

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

Restauration d'images en IRM anatomique pour l'étude préclinique des marqueurs du vieillissement cérébral

Les maladies neurovasculaires et neurodégénératives liées à l'âge sont en forte augmentation. Alors que ces changements pathologiques montrent des effets sur le cerveau avant l'apparition de symptômes cliniques, une meilleure compréhension du processus de vieillissement normal du cerveau aidera à distinguer l'impact des pathologies connues sur la structure régionale du cerveau. En outre, la connaissance des schémas de rétrécissement du cerveau dans le vieillissement normal pourrait conduire à une meilleure compréhension de ses causes et peut-être à des interventions réduisant la perte de fonctions cérébrales associée à l'atrophie cérébrale. Par conséquent, ce projet de thèse vise à détecter les biomarqueurs du vieillissement normal et pathologique du cerveau dans un modèle de primate non humain, le singe marmouset (Callithrix Jacchus), qui possède des caractéristiques anatomiques plus proches de celles des humains que de celles des rongeurs. Cependant, les changements structurels (par exemple, de volumes, d'épaisseur corticale) qui peuvent se produire au cours de leur vie adulte peuvent être minimes à l'échelle de l'observation. Dans ce contexte, il est essentiel de disposer de techniques d'observation offrant un contraste et une résolution spatiale suffisamment élevés et permettant des évaluations détaillées des changements morphométriques du cerveau associé au vieillissement. Cependant, l'imagerie de petits cerveaux dans une plateforme IRM 3T dédiée à l'homme est une tâche difficile car la résolution spatiale et le contraste obtenus sont insuffisants par rapport à la taille des structures anatomiques observées et à l'échelle des modifications attendues. Cette thèse vise à développer des méthodes de restauration d'image pour les images IRM précliniques qui amélioreront la robustesse des algorithmes de segmentation. L'amélioration de la résolution spatiale des images à un rapport signal/bruit constant limitera les effets de volume partiel dans les voxels situés à la frontière entre deux structures et permettra une meilleure segmentation tout en augmentant la reproductibilité des résultats. Cette étape d'imagerie computationnelle est cruciale pour une analyse morphométrique longitudinale fiable basée sur les voxels et l'identification de marqueurs anatomiques du vieillissement cérébral en suivant les changements de volume dans la matière grise, la matière blanche et le liquide cérébral.Age-related neurovascular and neurodegenerative diseases are increasing significantly. While such pathological changes show effects on the brain before clinical symptoms appear, a better understanding of the normal aging brain process will help distinguish known pathologies' impact on regional brain structure. Furthermore, knowledge of the patterns of brain shrinkage in normal aging could lead to a better understanding of its causes and perhaps to interventions reducing the loss of brain functions. Therefore, this thesis project aims to detect normal and pathological brain aging biomarkers in a non-human primate model, the marmoset monkey (Callithrix Jacchus) which possesses anatomical characteristics more similar to humans than rodents. However, structural changes (e.g., volumes, cortical thickness) that may occur during their adult life may be minimal with respect to the scale of observation. In this context, it is essential to have observation techniques that offer sufficiently high contrast and spatial resolution and allow detailed assessments of the morphometric brain changes associated with aging. However, imaging small brains in a 3T MRI platform dedicated to humans is a challenging task because the spatial resolution and the contrast obtained are insufficient compared to the size of the anatomical structures observed and the scale of the xpected changes with age. This thesis aims to develop image restoration methods for preclinical MR images that will improve the robustness of the segmentation algorithms. Improving the resolution of the images at a constant signal-to-noise ratio will limit the effects of partial volume in voxels located at the border between two structures and allow a better segmentation while increasing the results' reproducibility. This computational imaging step is crucial for a reliable longitudinal voxel-based morphometric analysis and for the identification of anatomical markers of brain aging by following the volume changes in gray matter, white matter and cerebrospinal fluid