Champs à phase aléatoire et champs gaussiens pour la mesure de netteté d’images et la synthèse rapide de textures

This thesis deals with the Fourier phase structure of natural images, and addresses no-reference sharpness assessment and fast texture synthesis by example. In Chapter 2, we present several models of random fields in a unified framework, like the spot noise model and the Gaussian model, with particular attention to the spectral representation of these random fields. In Chapter 3, random phase models are used to perform by-example synthesis of microtextures (textures with no salient features). We show that a microtexture can be summarized by a small image that can be used for fast and flexible synthesis based on the spot noise model. Besides, we address microtexture inpainting through the use of Gaussian conditional simulation. In Chapter 4, we present three measures of the global Fourier phase coherence. Their link with the image sharpness is established based on a theoretical and practical study. We then derive a stochastic optimization scheme for these indices, which leads to a blind deblurring algorithm. Finally, in Chapter 5, after discussing the possibility of direct phase analysis or synthesis, we propose two non random phase texture models which allow for synthesis of more structured textures and still have simple mathematical guarantees.Dans cette thèse, on étudie la structuration des phases de la transformée de Fourier d'images naturelles, ce qui, du point de vue applicatif, débouche sur plusieurs mesures de netteté ainsi que sur des algorithmes rapides pour la synthèse de texture par l'exemple. Le Chapitre 2 présente dans un cadre unifié plusieurs modèles de champs aléatoires, notamment les champs spot noise et champs gaussiens, en prêtant une attention particulière aux représentations fréquentielles de ces champs aléatoires. Le Chapitre 3 détaille l'utilisation des champs à phase aléatoire à la synthèse de textures peu structurées (microtextures). On montre qu'une microtexture peut être résumée en une image de petite taille s'intégrant à un algorithme de synthèse très rapide et flexible via le modèle spot noise. Aussi on propose un algorithme de désocclusion de zones texturales uniformes basé sur la simulation gaussienne conditionnelle. Le Chapitre 4 présente trois mesures de cohérence globale des phases de la transformée de Fourier. Après une étude théorique et pratique établissant leur lien avec la netteté d'image, on propose un algorithme de déflouage aveugle basé sur l'optimisation stochastique de ces indices. Enfin, dans le Chapitre 5, après une discussion sur l'analyse et la synthèse directe de l'information de phase, on propose deux modèles de textures à phases cohérentes qui permettent la synthèse de textures plus structurées tout en conservant quelques garanties mathématiques simples

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

Applications of Physically Accurate Deep Learning for Processing Digital Rock Images

Digital rock analysis aims to improve our understanding of the fluid flow properties of reservoir rocks, which are important for enhanced oil recovery, hydrogen storage, carbonate dioxide storage, and groundwater management. X-ray microcomputed tomography (micro-CT) is the primary approach to capturing the structure of porous rock samples for digital rock analysis. Initially, the obtained micro-CT images are processed using image-based techniques, such as registration, denoising, and segmentation depending on various requirements. Numerical simulations are then conducted on the digital models for petrophysical prediction. The accuracy of the numerical simulation highly depends on the quality of the micro-CT images. Therefore, image processing is a critical step for digital rock analysis. Recent advances in deep learning have surpassed conventional methods for image processing. Herein, the utility of convolutional neural networks (CNN) and generative adversarial networks (GAN) are assessed in regard to various applications in digital rock image processing, such as segmentation, super-resolution, and denoising. To obtain training data, different sandstone and carbonate samples were scanned using various micro-CT facilities. After that, validation images previously unseen by the trained neural networks are utilised to evaluate the performance and robustness of the proposed deep learning techniques. Various threshold scenarios are applied to segment the reconstructed digital rock images for sensitivity analyses. Then, quantitative petrophysical analyses, such as porosity, absolute/relative permeability, and pore size distribution, are implemented to estimate the physical accuracy of the digital rock data with the corresponding ground truth data. The results show that both CNN and GAN deep learning methods can provide physically accurate digital rock images with less user bias than traditional approaches. These results unlock new pathways for various applications related to the reservoir characterisation of porous reservoir rocks

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

Forum Bildverarbeitung 2016

Bildverarbeitung spielt in vielen Bereichen der Technik zur schnellen und berührungslosen Datenerfassung eine Schlüsselrolle. Der vorliegende Tagungsband des „Forums Bildverarbeitung“, das am 1. und 2. Dezember 2016 in Karlsruhe als Veranstaltung des Karlsruher Instituts für Technologie und des Fraunhofer-Instituts für Optronik, Systemtechnik und Bildauswertung stattfand, enthält die Aufsätze der eingegangenen Beiträge. Darin wird über aktuelle Trends und Lösungen der Bildverarbeitung berichtet