Le flou de mouvement de haute qualité est un effet de plus en plus important en rendu interactif. Avec l'augmentation constante en qualité des ressources et en fidélité des scènes vient un désir semblable pour des effets lenticulaires plus détaillés et réalistes. Cependant, même dans le contexte du rendu hors-ligne, le flou de mouvement est souvent approximé à l'aide d'un post-traitement. Les algorithmes de post-traitement pour le flou de mouvement ont fait des pas de géant au niveau de la qualité visuelle, générant des résultats plausibles tout en conservant un niveau de performance interactif. Néanmoins, des artefacts persistent en présence, par exemple, de mouvements superposés ou de motifs de mouvement à très large ou très fine échelle, ainsi qu'en présence de mouvement à la fois linéaire et rotationnel. De plus, des mouvements d'amplitude importante ont tendance à causer des artefacts évidents aux bordures d'objets ou d'image. Ce mémoire présente une technique qui résout ces artefacts avec un échantillonnage plus robuste et un système de filtrage qui échantillonne selon deux directions qui sont dynamiquement et automatiquement sélectionnées pour donner l'image la plus précise possible. Ces modifications entraînent un coût en performance somme toute mineur comparativement aux implantations existantes: nous pouvons générer un flou de mouvement plausible et temporellement cohérent pour plusieurs séquences d'animation complexes, le tout en moins de 2ms à une résolution de 1280 x 720. De plus, notre filtre est conçu pour s'intégrer facilement avec des filtres post-traitement d'anticrénelage. / High-quality motion blur is an increasingly important effect in interactive graphics. With the continuous increase in asset quality and scene fidelity comes a similar desire for more detailed and realistic lenticular effects. However, even in the context of offline rendering, motion blur is often approximated as a post process. Recent motion blur post-processes have made great leaps in visual quality, generating plausible results with interactive performance. Still, distracting artifacts remain in the presence of, for instance, overlapping motion or large- and fine-scale motion features, as well as in the presence of both linear and rotational motion. Furthermore, large scale motion often tends to cause obvious artifacts at boundary points. This thesis addresses these artifacts with a more robust sampling and filtering scheme that samples across two directions which are dynamically and automatically selected to provide the most accurate image possible. These modifications come at a very slight penalty compared to previous motion blur implementations: we render plausible, temporally-coherent motion blur on several complex animation sequences, all in under 2ms at a resolution of 1280 x 720. Moreover, our filter is designed to integrate seamlessly with post-process anti-aliasing.

This thesis provides theoretical and practical solutions to two problems raised by digital photography of moving scenes, and infrared photography. Until recently photographing moving objects could only be done using short exposure times. Yet, two recent groundbreaking works have proposed two new designs of camera allowing arbitrary exposure times. The flutter shutter of Agrawal et al. creates an invertible motion blur by using a clever shutter technique to interrupt the photon flux during the exposure time according to a well chosen binary sequence. The motion-invariant photography of Levin et al. gets the same result by accelerating the camera at a constant rate. Both methods follow computational photography as a new paradigm. The conception of cameras is rethought to include sophisticated digital processing. This thesis proposes a method for evaluating the image quality of these new cameras. The leitmotiv of the analysis is the SNR (signal to noise ratio) of the image after deconvolution. It gives the efficiency of these new camera design in terms of image quality. The theory provides explicit formulas for the SNR. It raises two paradoxes of these cameras, and resolves them. It provides the underlying motion model of each flutter shutter, including patented ones. A shorter second part addresses the the main quality problem in infrared video imaging, the non-uniformity. This perturbation is a time-dependent noise caused by the infrared sensor, structured in columns. The conclusion of this work is that it is not only possible but also efficient and robust to perform the correction on a single image. This permits to ensure the absence of ''ghost artifacts'', a classic of the literature on the subject, coming from inadequate processing relative to the acquisition model.

Avec l’intégration des appareils photos dans les appareils mobiles, leur démocratisation et la réduction de la taille de l’imageur, de l’optique et de la taille pixels, les photos sont de plus en plus sujettes au flou de bougé dû aux tremblements de la main. À cette tendance s’ajoute un accroissement constaté dans l’exigence de qualité d’image de la part des utilisateurs. Pour réduire ce flou, des systèmes de stabilisation d’image ont été développés. Néanmoins ceux-ci ne permettent pas de garantir la qualité de netteté des images et souffrent parfois d’une intégration limitée. En réponse à ces limitations, ces travaux de recherche proposent, d’une part, un modèle de tremblement physiologique permettant de simuler de manière fidèle les flous de bougé et, d’autre part, une étude sur la perception visuelle du flou permettant le développement d’une métrique de qualité. Enfin des architectures de stabilisations, exploitant ces nouveaux outils, sont proposées. Ces nouvelles architectures permettent de réduire le nombre de composants externes ainsi que de garantir la netteté des images stabilisées. / With the integration of cameras in mobile devices, their democratization and the reduction of the imager’s size, the optical system dimensions and the pixels miniaturization, the photos become more and more subject to motion blur due to the hand tremor. In addition, the requirements in terms of image quality become higher and higher. Hence, in order to reduce this blur, several image stabilization systems have been developed. Nevertheless, they cannot guarantee the sharpness quality of resulting images and in some cases, they show integration difficulties. In order to overcome these limitations, the research work presented in this thesis proposes, first of all, a physiological tremor model that aims to simulate realistic camera shake and secondly, presents a study on visual perception of blur. This study enables the development of a quality metric. Finally, stabilization algorithms and architectures exploiting these new tools are presented. These new architectures reduce the number of external components and ensure sharp stabilized images.

The thesis deals with the issue of tracking feature points earned from videosequences of hand helded camera. The work is focused on the case of moving camera and static background, and events that are associated with this case and can occur. There is studied the movement of the camera, which is given its direction and speed. The aim of this work is the election and the subsequent implementation of three fundamentally different methods suitable for tracking feature points in case of moving camera and their comparison according to set criteria. On the basis of comparison will be under pre-defined conditions chosen algorithm that is best able to deal with tracing these points.

In space exploration optical imaging is one of the key measurements conducted, with a vast majority of missions heavily relying on optical data acquisition to examine alien worlds. One such endeavor is ESA’s F-class mission Comet Interceptor, a multi-element spacecraft expected to be launched in 2028. It consists of a primary platform and two sub-spacecraft, one of which carrying the Optical Periscopic Imager for Comets (OPIC). An accurate prediction of the generated imagery is of undeniable importance as mission planning and instrument design strongly depend on the real-world output quality of the camera system. In the case of OPIC, the collected image data will be used to reconstruct three dimensional models of targeted celestial bodies. Furthermore, the sub-spacecraft faces a risk of high velocity dust impacts, leading to a limited number of data samples to be broadcasted back to the primary spacecraft before collision. Testing image prioritization algorithms and reconstruction methods prior to mission start requires accurate computer-generated images. Camera sensors and lens systems are subjected to various optical distortions and aberrations that degrade the final image. Popular render engines model those effects to a certain degree only and as a result produce content that is looking too perfect. While more sophisticated software products exist, they often come with compatibility limitations and other drawbacks. This report discusses the most important optical aberrations, as well as their relevance for optical instruments in space applications with a particular focus on the Comet Interceptor mission. The main part of this work is however the implementation of a dedicated software tool that simulates a variety of optical aberrations complementing the basic camera model of the Blender render engine. While its functionality is mostly demonstrated for OPIC, the software is designed with a broad range of usage scenarios in mind.

生活中在使用相機時，由於機器晃動或物體移動所造成的模糊影像時常可見。當影像模糊的成因是影像曝光時間內相機與拍攝物體相對線性移動時，則我們稱為線性動態模糊。理論上，模糊影像可以表示成原始影像與點擴散函數的旋積，本文的研究重點為點擴散函數中模糊參數的估計，雷登轉換將被運用在此問題上。我們首先介紹兩個現有方法，我們將探討這些方法中用來消除雜訊的步驟之適用性及必要性。另一方面，在模糊參數的估計過程中，我們在雷登轉換加入圓限制以及採用移動平均法。我們透過實驗證實，本篇提出的方法可以獲得更準確的估計結果以及更好的模糊影像還原效果。 / Nowadays, collecting a digital image becomes convenient and low-cost due to rapid progress in digital camera technology. Blurred images frequently appear because of camera shake or moving objects. There are several different types of blur. When the blur is caused by the linear motion between the object and the camera during the light exposure, it’s called a linear motion blur. Mathematically, a blurred image is expressed as a convolution of a point spread function and the original image. Our study considers Radon transform for the estimation of the point spread function. To improve the existing methods, a circle restriction and the moving average method are applied in the estimating procedure. Through intensive experiments, the proposed method is found enable to produce more accurate estimation and better performance in image restoration.

Cette thèse apporte des solutions théoriques et pratiques à deux problèmes soulevés par la photographie numérique en présence de mouvement, et par la photographie infrarouge. La photographie d'objets en mouvement semblait ne pouvoir se faire qu'avec des temps d'exposition très courts, jusqu'à ce que deux travaux révolutionnaires proposent deux nouveaux types de caméra permettant un temps d'exposition arbitraire. Le flutter shutter de Agrawal et al. crée en effet un flou inversible, grâce à un obturateur aux séquences d'ouverture-fermeture bie{\it n choisies. Le motion invariant photography de Levin et al. obtient ce même effet avec une accélération constante de la caméra. Les deux méthodes suivent ainsi un nouveau paradigme, la computational photography, selon lequel les caméras sont repensées, car elles incluent un traitement numérique sophistiqué. Cette thèse propose une méthode pour évaluer la qualité image des nouvelles caméras. Le fil conducteur de l'analyse est donc l'évaluation du SNR (signal to noise ratio) de l'image obtenue après déconvolution. La théorie fournit des formules explicites pour le SNR, soulève deux paradoxes de ces caméras, et les résout. Elle permet d'obtenir le modèle de mouvement sous-jacent à chaque flutter shutter, notamment tous ceux qui sont brevetés. Une seconde partie plus brève aborde le problème de qualité principal en imagerie vidéo infrarouge, la non-uniformité. Il s'agit d'un bruit évolutif et structuré en colonnes causé par le capteur. La conclusion des travaux est qu'il est non seulement possible mais également efficace et robuste d'effectuer la correction sur une seule image. Cela permet de contourner le problème récurrent des "ghost artifacts"résultant d'une incohérence du traitement par rapport au modèle d'acquisition. / This thesis provides theoretical and practical solutions to two problems raised by digital photography of moving scenes, and infrared photography. Until recently photographing moving objects could only be done using short exposure times. Yet, two recent groundbreaking works have proposed two new designs of camera allowing arbitrary exposure times. The flutter shutter of Agrawal et al. creates an invertible motion blur by using a clever shutter technique to interrupt the photon flux during the exposure time according to a well chosen binary sequence. The motion-invariant photography of Levin et al. gets the same result by accelerating the camera at a constant rate. Both methods follow computational photography as a new paradigm. The conception of cameras is rethought to include sophisticated digital processing. This thesis proposes a method for evaluating the image quality of these new cameras. The leitmotiv of the analysis is the SNR (signal to noise ratio) of the image after deconvolution. It gives the efficiency of these new camera design in terms of image quality. The theory provides explicit formulas for the SNR. It raises two paradoxes of these cameras, and resolves them. It provides the underlying motion model of each flutter shutter, including patented ones. A shorter second part addresses the the main quality problem in infrared video imaging, the non-uniformity. This perturbation is a time-dependent noise caused by the infrared sensor, structured in columns. The conclusion of this work is that it is not only possible but also efficient and robust to perform the correction on a single image. This permits to ensure the absence of ``ghost artifacts'', a classic of the literature on the subject, coming from inadequate processing relative to the acquisition model.

Computer vision algorithms typically process real world image data acquired by cameras or video cameras. Such image data suffer from imperfections cause by the acquisition process. This paper focuses on simulation of the acquisition process on simulation of the acquisition process in order to enable rendering of images based on a 3D generated model. Imperfections, such as geometry distorion, chromatic aberration, depth of field effect, motion blur, vignetting and lens flare are considered.

This thesis is concerned in the method of realistic rendering using a distributed raytracing. This method simulates various visual effects and generates high realistic 2D images. The work analyses the problem and explains principles of solution related to this technique. There is also descriprion of the method of simple reytracing which provides a basis for the distributed raytracing. A part of work is specialized for optimalization of distributed raytracing.

VYSOKÉ UČENÍ TECHNICKÉ V BRNĚ Distributed Ray Tracing, also called distribution ray tracing and stochastic ray tracing, is a refinement of ray tracing that allows for the rendering of "soft" phenomena, area light, depth of field and motion blur.