Structure-aware image denoising, super-resolution, and enhancement methods

Denoising, super-resolution and structure enhancement are classical image processing applications. The motive behind their existence is to aid our visual analysis of raw digital images. Despite tremendous progress in these fields, certain difficult problems are still open to research. For example, denoising and super-resolution techniques which possess all the following properties, are very scarce: They must preserve critical structures like corners, should be robust to the type of noise distribution, avoid undesirable artefacts, and also be fast. The area of structure enhancement also has an unresolved issue: Very little efforts have been put into designing models that can tackle anisotropic deformations in the image acquisition process. In this thesis, we design novel methods in the form of partial differential equations, patch-based approaches and variational models to overcome the aforementioned obstacles. In most cases, our methods outperform the existing approaches in both quality and speed, despite being applicable to a broader range of practical situations.Entrauschen, Superresolution und Strukturverbesserung sind klassische Anwendungen der Bildverarbeitung. Ihre Existenz bedingt sich in dem Bestreben, die visuelle Begutachtung digitaler Bildrohdaten zu unterstützen. Trotz erheblicher Fortschritte in diesen Feldern bedürfen bestimmte schwierige Probleme noch weiterer Forschung. So sind beispielsweise Entrauschungsund Superresolutionsverfahren, welche alle der folgenden Eingenschaften besitzen, sehr selten: die Erhaltung wichtiger Strukturen wie Ecken, Robustheit bezüglich der Rauschverteilung, Vermeidung unerwünschter Artefakte und niedrige Laufzeit. Auch im Gebiet der Strukturverbesserung liegt ein ungelöstes Problem vor: Bisher wurde nur sehr wenig Forschungsaufwand in die Entwicklung von Modellen investieret, welche anisotrope Deformationen in bildgebenden Verfahren bewältigen können. In dieser Arbeit entwerfen wir neue Methoden in Form von partiellen Differentialgleichungen, patch-basierten Ansätzen und Variationsmodellen um die oben erwähnten Hindernisse zu überwinden. In den meisten Fällen übertreffen unsere Methoden nicht nur qualitativ die bisher verwendeten Ansätze, sondern lösen die gestellten Aufgaben auch schneller. Zudem decken wir mit unseren Modellen einen breiteren Bereich praktischer Fragestellungen ab

Modeling the Effect of Images on Product Choices

Conjoint is one of the most popular methods in marketing research, widely used to understand how customers trade-off features of a product. Since product images have a strong influence on customer choice, it is natural to want to include images in conjoint studies, yet this has proven to be difficult, since images are difficult to parsimoniously characterize in the utility function. This paper proposes a novel approach to account for the effect of images on respondents’ choices, in which consumer heterogeneity in the appeal of the images is modeled through the covariance structure in a probit model. The covariance structure is informed by a separate task where respondents rate the images included in the study. In our application to midsize crossover vehicles, we show that our approach readily scales to a large number of images, fits better than several alternatives commonly used in practice, and makes more reasonable predictions about product substitution when a new product enters the market. We discuss how this approach could be used predict the effect of other difficult-to-characterize product attribute such as sound quality or taste on product choice

The role of temporal frequency in continuous flash suppression: A case for a unified framework

In continuous flash suppression (CFS), a rapidly changing Mondrian sequence is presented to one eye in order to suppress a static target presented to the other eye. Targets generally remain suppressed for several seconds at a time, contributing to the widespread use of CFS in studies of unconscious visual processes. Nevertheless, the mechanisms underlying CFS suppression remain unclear, complicating its use and the comprehension of results obtained with the technique. As a starting point, this thesis examined the role of temporal frequency in CFS suppression using carefully controlled stimuli generated by Fourier Transform techniques. A low-level stimulus attribute, the choice of temporal frequency allowed us to evaluate the contributions of early visual processes and test the general assumption that fast update rates drive CFS effectiveness. Three psychophysical studies are described in this thesis, starting with the temporal frequency tuning of CFS (Chapter 2), the relationship between the Mondrian pattern and temporal frequency content (Chapter 3), and finally the role of temporal frequency selectivity in CFS (Chapter 4). Contrary to conventional wisdom, the results showed that the suppression of static targets is largely driven by high spatial frequencies and low temporal frequencies. Faster masker rates, on the other hand, worked best with transient targets. Indicative of early, feature selective processes, these findings are reminiscent of binocular rivalry suppression, demonstrating the possible use of a unified framework

On computing global similarity in images

The retrieval of images based on their visual similarity to an example image is an important and fascinating area of research. Here, a method to characterize visual appearance for determining global similarity in images is described. Images are filtered with Gaussian derivatives and geometric features are computed from the filtered images. The geometric features used here are curvature and phase. Two images may be said to be similar if they have similar distributions of such features. Global similarity may, therefore, be deduced by comparing histograms of these features. This allows for rapid retrieval and examples from collection of gray-level and trademark images are show

Interactive Evolutionary Algorithms for Image Enhancement and Creation

Image enhancement and creation, particularly for aesthetic purposes, are tasks for which the use of interactive evolutionary algorithms would seem to be well suited. Previous work has concentrated on the development of various aspects of the interactive evolutionary algorithms and their application to various image enhancement and creation problems. Robust evaluation of algorithmic design options in interactive evolutionary algorithms and the comparison of interactive evolutionary algorithms to alternative approaches to achieving the same goals is generally less well addressed. The work presented in this thesis is primarily concerned with different interactive evolutionary algorithms, search spaces, and operators for setting the input values required by image processing and image creation tasks. A secondary concern is determining when the use of the interactive evolutionary algorithm approach to image enhancement problems is warranted and how it compares with alternative approaches. Various interactive evolutionary algorithms were implemented and compared in a number of specifically devised experiments using tasks of varying complexity. A novel aspect of this thesis, with regards to other work in the study of interactive evolutionary algorithms, was that statistical analysis of the data gathered from the experiments was performed. This analysis demonstrated, contrary to popular assumption, that the choice of algorithm parameters, operators, search spaces, and even the underlying evolutionary algorithm has little effect on the quality of the resulting images or the time it takes to develop them. It was found that the interaction methods chosen when implementing the user interface of the interactive evolutionary algorithms had a greater influence on the performances of the algorithms

Highlighting dissimilarity in medical images using hierarchical clustering based segmentation (HCS).

Tissue abnormality in a medical image is usually related to a dissimilar part of an otherwise homogeneous image. The dissimilarity may be subtle or strong depending on the medical modality and the type of abnormal tissue. Dissimilarity within an otherwise homogeneous area of an image may not always be due to tissue abnormality. It might be due to image noise or due to variability within the same tissue type. Given this situation it is almost impossible to design and implement a generic segmentation process that will consistently give a single appropriate solution under all conditions. Hence a dissimilarity highlighting process that yields a hierarchy of segmentation results is more useful. This would benefit from high level human interaction to select the appropriate image segmentation for a particular application, because one of the capabilities of the human vision process when visualising images is its ability to visualise them at different levels of details.The purpose of this thesis is to design and implement a segmentation procedure to resemble the capability of the human vision system's ability to generate multiple solutions of varying resolutions. To this end, the main objectives for this study were: (i) to design a segmentation process that would be unsupervised and completely data driven. (ii) to design a segmentation process that would automatically and consistently generate a hierarchy of segmentation results. In order to achieve these objectives a hierarchical clustering based segmentation (HCS) process was designed and implemented. The developed HCS process partitioned the images into their constituent regions at hierarchical levels of allowable dissimilarity between the different spatially adjacent or disjoint regions. At any particular level in the hierarchy the segmentation process clustered together all the pixels and/or regions that had dissimilarity among them which was less than or equal to the dissimilarity allowed for that level. The clustering process was designed in such a way that the merging of the clusters did not depend on the order in which the clusters were evaluated.The HCS process developed was used to process images of different medical modalities and the results obtained are summarised below: (i) It was successfully used to highlight hard to visualise stroke affected areas in T2 weighted MR images confirmed by the diffusion weighted scans of the same areas of the brain. (ii) It was used to highlight dissimilarities in the MRI, CT and ultrasound images and the results were validated by the radiologists. It processed medical image data and consistently produced a hierarchy of segmentation results but did not give a diagnosis. This was left for the experts to make use of the results and incorporate these with their own knowledge to arrive upon a diagnosis. Thus the process acts as an effective computer aided detection (CAD) tool.The unique features of the designed and implemented HCS process are: (i) The segmentation process is unsupervised, completely data driven and can be applied to any medical modality, with equal success, without any prior information about the image data(ii) The merging routines can evaluate and merge spatially adjacent and disjoint similar regions and consistently give a hierarchy of segmentation results. (iii) The designed merging process can yield crisp border delineation between the regions

Algorithmic Information Theory Applications in Bright Field Microscopy and Epithelial Pattern Formation

Algorithmic Information Theory (AIT), also known as Kolmogorov complexity, is a quantitative approach to defining information. AIT is mainly used to measure the amount of information present in the observations of a given phenomenon. In this dissertation we explore the applications of AIT in two case studies. The first examines bright field cell image segmentation and the second examines the information complexity of multicellular patterns. In the first study we demonstrate that our proposed AIT-based algorithm provides an accurate and robust bright field cell segmentation. Cell segmentation is the process of detecting cells in microscopy images, which is usually a challenging task for bright field microscopy due to the low contrast of the images. In the second study, which is the primary contribution of this dissertation, we employ an AIT-based algorithm to quantify the complexity of information content that arises during the development of multicellular organisms. We simulate multicellular organism development by coupling the Gene Regulatory Networks (GRN) within an epithelial field. Our results show that the configuration of GRNs influences the information complexity in the resultant multicellular patterns