The continuous expansion of technology, broadband connectivity and the wide range of new devices in the IoT cause serious concerns regarding privacy and security. In addition, in the IoT a key challenge is the storage and management of massive data streams. For example, there is always the demand for acceptable size with the highest quality possible for images to meet the rapidly increasing number of multimedia applications. The effort in this dissertation contributes to the resolution of concerns related to the security and compression functions in image communications in the Internet of Thing (IoT), due to the fast of evolution of IoT. This dissertation proposes frameworks for a secure digital camera in the IoT. The objectives of this dissertation are twofold. On the one hand, the proposed framework architecture offers a double-layer of protection: encryption and watermarking that will address all issues related to security, privacy, and digital rights management (DRM) by applying a hardware architecture of the state-of-the-art image compression technique Better Portable Graphics (BPG), which achieves high compression ratio with small size. On the other hand, the proposed framework of SBPG is integrated with the Digital Camera. Thus, the proposed framework of SBPG integrated with SDC is suitable for high performance imaging in the IoT, such as Intelligent Traffic Surveillance (ITS) and Telemedicine. Due to power consumption, which has become a major concern in any portable application, a low-power design of SBPG is proposed to achieve an energy- efficient SBPG design. As the visual quality of the watermarked and compressed images improves with larger values of PSNR, the results show that the proposed SBPG substantially increases the quality of the watermarked compressed images. Higher value of PSNR also shows how robust the algorithm is to different types of attack. From the results obtained for the energy- efficient SBPG design, it can be observed that the power consumption is substantially reduced, up to 19%.

The topic of this thesis is registration of image sequences captured by experimental ophthalmoscope. It contains anatomical description of the visual system as well as the description of functions of selected ophthalmoscopic devices. The next covered topic is theoretical summary of registration process, which is followed by an overview of the used methods, which forms the basis of the design and implementation of the registration algorithm in the Python programming language. After implementation, the accuracy and computational complexity of a registration was evaluated. Tests of optimalization of the proposed approach were performed with regards to the obtained results, through which sufficiently accurate registration has been achieved, evaluated on the basis of Euclidean distances, standard deviation and visual observation. In case of high-quality recorded sequences, values of Euclidean distances ranged from 0.60 to 4.07 pixels on the contrary, values higher than 20 pixels occurred in the case of poor-quality recordings. Standard deviation values in recordings with high enough resolution have not reached worse results than 4.12.

Nuclear fuel is visually inspected during regular shutdowns in order to monitor defects and long-term changes. To enable automatic comparison of images of fuel assemblies, it is crucial to perform their registration, the implementation of which has not yet been published in the scientific literature. In this work we present an analysis of image registration techniques and similarity metrics inspired by the focus operators used in autofocus and shape-from-focus. Their performance has been evaluated using a series of experiments that tested their various properties on a novel data set obtained in cooperation with the research organization Centrum výzkumu Řež. Finally, we present and discuss the results and make recommendations on which to use in which scenario.

The authors have during the spring semester 2011 studied the subject destination development during a political crisis, focusing on Tunisia. At the beginning of 2011 the country was in an uncertain political situation when the revolution of the Tunisian people occurred. This created massive demonstrations on the streets, and resulted in the current government's resignation. More effects of the revolution has been seen in a drastic decline of inbound tourists in the country, which in turn affected the country's population and economy. From this point on Tunisia as a destination needs to recover to once again become attractive to tourists. Further revolutions in neighboring countries such as Libya, have contributed to the tourists' reluctance to travel to Tunisia. From this point of view, the tourist operators stand in front of a challenge in how they best can highlight the destination, and the positive impacts the revolution has contributed with. The purpose of this paper is from a destination development perspective examine how organizations in Sweden work to regain tourists to Tunisia after a period of decline. To answer this purpose, the authors use of these following questions: How have the revolution of the people in the spring of 2011 affected the tourism industry in Tunisia? How do the various tourist operators in Sweden relate to the revolution of the people in Tunisia 2011? Could it be that this revolution in spite of demonstrations and unrest contributes to something positive for the tourism industry in Tunisia? The methodology used in this paper is qualitative in nature, where a number of respondents in Sweden were interviewed to seek answers to selected questions. A constructivist approach was applied in which reality is seen as constantly changing and is created by ongoing processes that change over time. Destination Development, image and recovery are the key concepts covered in the theoretical framework. Destinations may occur at various stages in its life cycle. This is to portray how a destination can evolve to stagnation and at worst die out. Tunisia is in a phase of decline after the revolution in which people are waiting for tourists to return to the country. The results of the survey show that the revolution in 2011 has affected the tourism industry in that it has contributed to a decline of inbound tourists. The country´s image has been affected and there is now a challenge for organizations in Sweden to improve this image to regain tourists from Sweden to Tunisia. The vision for Tunisia as a destination is that despite the great social changes create a stable and democratic society, and highlight the revolutionary message of a new, more open Tunisia, which in turn creates attractiveness for the tourism industry.

Image processing consists of a series of tasks which widely appear in many areas. It can be used for processing photos taken by people's cameras, astronomy radio, radar imaging, medical devices and tomography. Among these tasks, image segmentation is a fundamental task in a series of applications. Image segmentation is so important that it attracts hundreds of thousands of researchers from lots of fields all over the world. Given an image, the goal of image segmentation is to classify all pixels into several classes. Given an image defined over a domain, the segmentation task is to divide the domain into several different sub-domains such that pixels in each sub-domain share some common information. Variational methods showcase their performance in all kinds of image processing problems, such as image denoising, image debluring, image segmentation and so on. They can preserve structures of images well. In recent decades, it is more and more popular to reformulate an image processing problem into an energy minimization problem. The problem is then minimized by some optimization based methods. Meanwhile, convolutional neural networks (CNNs) gain outstanding achievements in a wide range of fields such as image processing, nature language processing and video recognition. CNNs are data-driven techniques which often need large datasets for training comparing to other methods like variational based methods. When handling image processing tasks with large scale datasets, CNNs are the first selections due to their superior performances. However, the class of each pixel is predicted independently in semantic segmentation tasks which are dense classification problems. Spatial regularity of the segmented objects is still a problem for these methods. Especially when given few training data, CNNs could not perform well in the details. Isolated and scattered small regions often appear in all kinds of CNN segmentation results. In this thesis, we successfully add spatial regularization to the segmented objects. In our methods, spatial regularization such as total variation (TV) can be easily integrated into CNNs and they produce smooth edges and eliminates isolated points. Spatial dependency is a very important prior for many image segmentation tasks. Generally, convolutional operations are building blocks that process one local neighborhood at a time, which means CNNs usually don't explicitly make use of the spatial prior on image segmentation tasks. Empirical evaluations of the regularized neural networks on a series of image segmentation datasets show its good performance and ability in improving the performance of many image segmentation CNNs. We also design a recurrent structure which is composed of multiple TV blocks. By applying this structure to a popular segmentation CNN, the segmentation results are further improved. This is an end-to-end framework to regularize the segmentation results. The proposed framework could give smooth edges and eliminate isolated points. Comparing to other post-processing methods, our method needs little extra computation thus is effective and efficient. Since long range dependency is also very important for semantic segmentation, we further present non-local regularized softmax activation function for semantic image segmentation tasks. We introduce graph operators into CNNs by integrating nonlocal total variation regularizer into softmax activation function. We find the non-local regularized softmax activation function by the primal-dual hybrid gradient method. Experiments show that non-local regularized softmax activation function can bring regularization effect and preserve object details at the same time

Image signal processors (ISPs) are historically grown legacy software systems for reconstructing color images from noisy raw sensor measurements. They are usually composited of many heuristic blocks for denoising, demosaicking, and color restoration. Color reproduction in this context is of particular importance, since the raw colors are often severely distorted, and each smart phone manufacturer has developed their own characteristic heuristics for improving the color rendition, for example of skin tones and other visually important colors. In recent years there has been strong interest in replacing the historically grown ISP systems with deep learned pipelines. Much progress has been made in approximating legacy ISPs with such learned models. However, so far the focus of these efforts has been on reproducing the structural features of the images, with less attention paid to color rendition. Here we present Color Rendition ISP (CRISPnet), the first learned ISP model to specifically target color rendition accuracy relative to a complex, legacy smart phone ISP. We achieve this by utilizing both image metadata (like a legacy ISP would), as well as by learning simple global semantics based on image classification – similar to what a legacy ISP does to determine the scene type. We also contribute a new ISP image dataset consisting of both high dynamic range monitor data, as well as real-world data, both captured with an actual cell phone ISP pipeline under a variety of lighting conditions, exposure times, and gain settings.

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