Confidence driven TGV fusion

We introduce a novel model for spatially varying variational data fusion, driven by point-wise confidence values. The proposed model allows for the joint estimation of the data and the confidence values based on the spatial coherence of the data. We discuss the main properties of the introduced model as well as suitable algorithms for estimating the solution of the corresponding biconvex minimization problem and their convergence. The performance of the proposed model is evaluated considering the problem of depth image fusion by using both synthetic and real data from publicly available datasets

Fusion of Urban TanDEM-X raw DEMs using variational models

Recently, a new global Digital Elevation Model (DEM) with pixel spacing of 0.4 arcseconds and relative height accuracy finer than 2m for flat areas (slopes 20%) was created through the TanDEM-X mission. One important step of the chain of global DEM generation is to mosaic and fuse multiple raw DEM tiles to reach the target height accuracy. Currently, Weighted Averaging (WA) is applied as a fast and simple method for TanDEM-X raw DEM fusion in which the weights are computed from height error maps delivered from the Interferometric TanDEM-X Processor (ITP). However, evaluations show that WA is not the perfect DEM fusion method for urban areas especially in confrontation with edges such as building outlines. The main focus of this paper is to investigate more advanced variational approaches such as TV-L1 and Huber models. Furthermore, we also assess the performance of variational models for fusing raw DEMs produced from data takes with different baseline configurations and height of ambiguities. The results illustrate the high efficiency of variational models for TanDEM-X raw DEM fusion in comparison to WA. Using variational models could improve the DEM quality by up to 2m particularly in inner-city subsets.Comment: This is the pre-acceptance version, to read the final version, please go to IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing on IEEE Xplor

Context-based urban terrain reconstruction from uav-videos for geoinformation applications

Urban terrain reconstruction has many applications in areas of civil engineering, urban planning, surveillance and defense research. Therefore the needs of covering ad-hoc demand and performing a close-range urban terrain reconstruction with miniaturized and relatively inexpensive sensor platforms are constantly growing. Using (miniaturized) unmanned aerial vehicles, (M) UAVs, represents one of the most attractive alternatives to conventional large-scale aerial imagery. We cover in this paper a four-step procedure of obtaining georeferenced 3D urban models from video sequences. The four steps of the procedure - orientation, dense reconstruction, urban terrain modeling and geo-referencing - are robust, straight-forward, and nearly fully-automatic. The two last steps - namely, urban terrain modeling from almost-nadir videos and co-registration of models - represent the main contribution of this work and will therefore be covered with more detail. The essential substeps of the third step include digital terrain model (DTM) extraction, segregation of buildings from vegetation, as well as instantiation of building and tree models. The last step is subdivided into quasi-intrasensorial registration of Euclidean reconstructions and intersensorial registration with a geo-referenced orthophoto. Finally, we present reconstruction results from a real data-set and outline ideas for future work

Desarrollo de una aplicación software para la fusión robusta de mapas de profundidad y reconstrucción 3D densa

El objetivo principal de este proyecto es mejorar la calidad de los mapas de profundidad que se han obtenido a partir de un conjunto de imágenes en niveles gris capturadas por una cámara monocular. Un mapa de profundidad es simplemente una imagen en la que los píxeles, en vez de almacenar colores, almacenan la profundidad a la que se encuentran los objetos en la escena. El ámbito de aplicación de estos mapas de profundidad es muy variado, reconstrucción 3D, detección de obstáculos (útil por ejemplo para vehículos), algoritmos de tracking de cámara, segmentación de una escena (identificación de distintos objetos que la componen), etc. La mejora propuesta en este proyecto consiste en en la aplicación de un algoritmo de fusión de mapas de profundidad llamado TGV-fusion, para ello se van a utilizar técnicas variacionales de optimización densas, es decir, tienen en cuenta información en toda la imagen. La principal ventaja de esta técnica es que son idóneas para su programación sobre hardware paralelo, en particular en este trabajo se utiliza la arquitectura CUDA (Compute Unified Device Architecture) desarrollada por NVIDIA que extiende el lenguaje de programación C para codificar algoritmos paralelos en GPUs según el paradigma SIMT (Single Instruction Multiple Thread), es decir, una misma instrucción ejecutada en múltiples threads simultáneamente. Debido a la complejidad matemática de los algoritmos involucrados y a las dificultades iniciales propias de la falta de experiencia programando en CUDA, se tomó la decisión en los inicios del proyecto, de resolver primero el problema de image denoising (eliminación de ruido en imágenes) debido a que el algoritmo de esta solución está muy relacionado con el de la fusión de mapas pero es más sencillo de resolver e interpretar de forma intuitiva. En cuanto a los resultados generados en este proyecto son, en primer lugar la explicación intuitiva de los métodos matemáticos necesarios, definición del algoritmo de denoising para distintas normas, implementación de una aplicación para el estudio del efecto de los parámetros en este algoritmo, aplicación en tiempo real y aplicación final del TGV-fusión

Inverse problem theory in shape and action modeling

In this thesis we consider shape and action modeling problems under the perspective of inverse problem theory. Inverse problem theory proposes a mathematical framework for solving model parameter estimation problems. Inverse problems are typically ill-posed, which makes their solution challenging. Regularization theory and Bayesian statistical methods, which are proposed in the context of inverse problem theory, provide suitable methods for dealing with ill-posed problems. Regarding the application of inverse problem theory in shape and action modeling, we first discuss the problem of saliency prediction, considering a model proposed by the coherence theory of attention. According to coherence theory, salience regions emerge via proto-objects which we model using harmonic functions (thin-membranes). We also discuss the modeling of the 3D scene, as it is fundamental for extracting suitable scene features, which guide the generation of proto-objects. The next application we consider is the problem of image fusion. In this context, we propose a variational image fusion framework, based on confidence driven total variation regularization, and we consider its application to the problem of depth image fusion, which is an important step in the dense 3D scene reconstruction pipeline. The third problem we encounter regards action modeling, and in particular the recognition of human actions based on 3D data. Here, we employ a Bayesian nonparametric model to capture the idiosyncratic motions of the different body parts. Recognition is achieved by comparing the motion behaviors of the subject to a dictionary of behaviors for each action, learned by examples collected from other subjects. Next, we consider the 3D modeling of articulated objects from images taken from the web, with application to the 3D modeling of animals. By decomposing the full object in rigid components and by considering different aspects of these components, we model the object up this hierarchy, in order to obtain a 3D model of the entire object. Single view 3D modeling as well as model registration is performed, based on regularization methods. The last problem we consider, is the modeling of 3D specular (non-Lambertian) surfaces from a single image. To solve this challenging problem we propose a Bayesian non-parametric model for estimating the normal field of the surface from its appearance, by identifying the material of the surface. After computing an initial model of the surface, we apply regularization of its normal field considering also a photo-consistency constraint, in order to estimate the final shape of the surface. Finally, we conclude this thesis by summarizing the most significant results and by suggesting future directions regarding the application of inverse problem theory to challenging computer vision problems, as the ones encountered in this work

동적 환경 디블러링을 위한 새로운 모델, 알로기즘, 그리고 해석에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 8. 이경무.Blurring artifacts are the most common flaws in photographs. To remove these artifacts, many deblurring methods which restore sharp images from blurry ones have been studied considerably in the field of computational photography. However, state-of-the-art deblurring methods are based on a strong assumption that the captured scenes are static, and thus a great many things still remain to be done. In particular, these conventional methods fail to deblur blurry images captured in dynamic environments which have spatially varying blurs caused by various sources such as camera shake including out-of-plane motion, moving objects, depth variation, and so on. Therefore, the deblurring problem becomes more difficult and deeply challenging for dynamic scenes. Therefore, in this dissertation, addressing the deblurring problem of general dynamic scenes is a goal, and new solutions are introduced, that remove spatially varying blurs in dynamic scenes unlike conventional methods built on the assumption that the captured scenes are static. Three kinds of dynamic scene deblurring methods are proposed to achieve this goal, and they are based on: (1) segmentation, (2) sharp exemplar, (3) kernel-parametrization. The proposed approaches are introduced from segment-wise to pixel-wise approaches, and pixel-wise varying general blurs are handled in the end. First, the segmentation-based deblurring method estimates the latent image, multiple different kernels, and associated segments jointly. With the aid of the joint approach, segmentation-based method could achieve accurate blur kernel within a segment, remove segment-wise varying blurs, and reduce artifacts at the motion boundaries which are common in conventional approaches. Next, an \textit{exemplar}-based deblurring method is proposed, which utilizes a sharp exemplar to estimate highly accurate blur kernel and overcomes the limitations of the segmentation-based method that cannot handle small or texture-less segments. Lastly, the deblurring method using kernel-parametrization approximates the locally varying kernel as linear using motion flows. Thus the proposed method based on kernel-parametrization is generally applicable to remove pixel-wise varying blurs, and estimates the latent image and motion flow at the same time. With the proposed methods, significantly improved deblurring qualities are achieved, and intensive experimental evaluations demonstrate the superiority of the proposed methods in dynamic scene deblurring, in which state-of-the-art methods fail to deblur.Chapter 1 Introduction 1 Chapter 2 Image Deblurring with Segmentation 7 2.1 Introduction and Related Work 7 2.2 Segmentation-based Dynamic Scene Deblurring Model 11 2.2.1 Adaptive blur model selection 13 2.2.2 Regularization 14 2.3 Optimization 17 2.3.1 Sharp image restoration 18 2.3.2 Weight estimation 19 2.3.3 Kernel estimation 23 2.3.4 Overall procedure 25 2.4 Experiments 25 2.5 Summary 27 Chapter 3 Image Deblurring with Exemplar 33 3.1 Introduction and Related Work 35 3.2 Method Overview 37 3.3 Stage I: Exemplar Acquisition 38 3.3.1 Sharp image acquisition and preprocessing 38 3.3.2 Exemplar from blur-aware optical flow estimation 40 3.4 Stage II: Exemplar-based Deblurring 42 3.4.1 Exemplar-based latent image restoration 43 3.4.2 Motion-aware segmentation 44 3.4.3 Robust kernel estimation 45 3.4.4 Unified energy model and optimization 47 3.5 Stage III: Post-processing and Refinement 47 3.6 Experiments 49 3.7 Summary 53 Chapter 4 Image Deblurring with Kernel-Parametrization 57 4.1 Introduction and Related Work 59 4.2 Preliminary 60 4.3 Proposed Method 62 4.3.1 Image-statistics-guided motion 62 4.3.2 Adaptive variational deblurring model 64 4.4 Optimization 69 4.4.1 Motion estimation 70 4.4.2 Latent image restoration 72 4.4.3 Kernel re-initialization 73 4.5 Experiments 75 4.6 Summary 80 Chapter 5 Video Deblurring with Kernel-Parametrization 87 5.1 Introduction and Related Work 87 5.2 Generalized Video Deblurring 93 5.2.1 A new data model based on kernel-parametrization 94 5.2.2 A new optical flow constraint and temporal regularization 104 5.2.3 Spatial regularization 105 5.3 Optimization Framework 107 5.3.1 Sharp video restoration 108 5.3.2 Optical flows estimation 109 5.3.3 Defocus blur map estimation 110 5.4 Implementation Details 111 5.4.1 Initialization and duty cycle estimation 112 5.4.2 Occlusion detection and refinement 113 5.5 Motion Blur Dataset 114 5.5.1 Dataset generation 114 5.6 Experiments 116 5.7 Summary 120 Chapter 6 Conclusion 127 Bibliography 131 국문 초록 141Docto

Variational image fusion

The main goal of this work is the fusion of multiple images to a single composite that offers more information than the individual input images. We approach those fusion tasks within a variational framework. First, we present iterative schemes that are well-suited for such variational problems and related tasks. They lead to efficient algorithms that are simple to implement and well-parallelisable. Next, we design a general fusion technique that aims for an image with optimal local contrast. This is the key for a versatile method that performs well in many application areas such as multispectral imaging, decolourisation, and exposure fusion. To handle motion within an exposure set, we present the following two-step approach: First, we introduce the complete rank transform to design an optic flow approach that is robust against severe illumination changes. Second, we eliminate remaining misalignments by means of brightness transfer functions that relate the brightness values between frames. Additional knowledge about the exposure set enables us to propose the first fully coupled method that jointly computes an aligned high dynamic range image and dense displacement fields. Finally, we present a technique that infers depth information from differently focused images. In this context, we additionally introduce a novel second order regulariser that adapts to the image structure in an anisotropic way.Das Hauptziel dieser Arbeit ist die Fusion mehrerer Bilder zu einem Einzelbild, das mehr Informationen bietet als die einzelnen Eingangsbilder. Wir verwirklichen diese Fusionsaufgaben in einem variationellen Rahmen. Zunächst präsentieren wir iterative Schemata, die sich gut für solche variationellen Probleme und verwandte Aufgaben eignen. Danach entwerfen wir eine Fusionstechnik, die ein Bild mit optimalem lokalen Kontrast anstrebt. Dies ist der Schlüssel für eine vielseitige Methode, die gute Ergebnisse für zahlreiche Anwendungsbereiche wie Multispektralaufnahmen, Bildentfärbung oder Belichtungsreihenfusion liefert. Um Bewegungen in einer Belichtungsreihe zu handhaben, präsentieren wir folgenden Zweischrittansatz: Zuerst stellen wir die komplette Rangtransformation vor, um eine optische Flussmethode zu entwerfen, die robust gegenüber starken Beleuchtungsänderungen ist. Dann eliminieren wir verbleibende Registrierungsfehler mit der Helligkeitstransferfunktion, welche die Helligkeitswerte zwischen Bildern in Beziehung setzt. Zusätzliches Wissen über die Belichtungsreihe ermöglicht uns, die erste vollständig gekoppelte Methode vorzustellen, die gemeinsam ein registriertes Hochkontrastbild sowie dichte Bewegungsfelder berechnet. Final präsentieren wir eine Technik, die von unterschiedlich fokussierten Bildern Tiefeninformation ableitet. In diesem Kontext stellen wir zusätzlich einen neuen Regularisierer zweiter Ordnung vor, der sich der Bildstruktur anisotrop anpasst

Variational image fusion

The main goal of this work is the fusion of multiple images to a single composite that offers more information than the individual input images. We approach those fusion tasks within a variational framework. First, we present iterative schemes that are well-suited for such variational problems and related tasks. They lead to efficient algorithms that are simple to implement and well-parallelisable. Next, we design a general fusion technique that aims for an image with optimal local contrast. This is the key for a versatile method that performs well in many application areas such as multispectral imaging, decolourisation, and exposure fusion. To handle motion within an exposure set, we present the following two-step approach: First, we introduce the complete rank transform to design an optic flow approach that is robust against severe illumination changes. Second, we eliminate remaining misalignments by means of brightness transfer functions that relate the brightness values between frames. Additional knowledge about the exposure set enables us to propose the first fully coupled method that jointly computes an aligned high dynamic range image and dense displacement fields. Finally, we present a technique that infers depth information from differently focused images. In this context, we additionally introduce a novel second order regulariser that adapts to the image structure in an anisotropic way.Das Hauptziel dieser Arbeit ist die Fusion mehrerer Bilder zu einem Einzelbild, das mehr Informationen bietet als die einzelnen Eingangsbilder. Wir verwirklichen diese Fusionsaufgaben in einem variationellen Rahmen. Zunächst präsentieren wir iterative Schemata, die sich gut für solche variationellen Probleme und verwandte Aufgaben eignen. Danach entwerfen wir eine Fusionstechnik, die ein Bild mit optimalem lokalen Kontrast anstrebt. Dies ist der Schlüssel für eine vielseitige Methode, die gute Ergebnisse für zahlreiche Anwendungsbereiche wie Multispektralaufnahmen, Bildentfärbung oder Belichtungsreihenfusion liefert. Um Bewegungen in einer Belichtungsreihe zu handhaben, präsentieren wir folgenden Zweischrittansatz: Zuerst stellen wir die komplette Rangtransformation vor, um eine optische Flussmethode zu entwerfen, die robust gegenüber starken Beleuchtungsänderungen ist. Dann eliminieren wir verbleibende Registrierungsfehler mit der Helligkeitstransferfunktion, welche die Helligkeitswerte zwischen Bildern in Beziehung setzt. Zusätzliches Wissen über die Belichtungsreihe ermöglicht uns, die erste vollständig gekoppelte Methode vorzustellen, die gemeinsam ein registriertes Hochkontrastbild sowie dichte Bewegungsfelder berechnet. Final präsentieren wir eine Technik, die von unterschiedlich fokussierten Bildern Tiefeninformation ableitet. In diesem Kontext stellen wir zusätzlich einen neuen Regularisierer zweiter Ordnung vor, der sich der Bildstruktur anisotrop anpasst