The Blind Image Deconvolution/Deblurring (BID) problem was realised in the early 1960s but it still remains a challenging task for the image processing research community to find an efficient, reliable and most importantly a diversely applicable deblurring scheme. The main challenge arises from little or no prior information about the image or the blurring process as well as the lack of optimal restoration filters to reduce or completely eliminate the blurring effect. Moreover, restoration can be marred by the two common side effects of deblurring; namely the noise amplification and ringing artefacts that arise in the deblurred image due to an unrealizable or imperfect restoration filter. Also, developing a scheme that can process different types of blur, especially for real images, is yet to be realized to a satisfactory level. This research is focused on the development of blind restoration schemes for real life blurred images. The primary objective is to design a BID scheme that is robust in term of Point Spread Function (PSF) estimation, efficient in terms of restoration speed, and effective in terms of restoration quality. A desired scheme will require a deblurring measure to act as a feedback of quality regarding the deblurred image and lead the estimation of the blurring PSF. The blurred image and the estimated PSF can then be passed on to any classical restoration filter for deblurring. The deblurring measures presented in this research include blind non-Gaussianity measures as well as blind Image Quality Measures (IQMs). These measures are blind in the sense that they are able to gauge the quality of an image directly from it without the need to reference a high quality image. The non-Gaussianity measures include spatial and spectral kurtosis measures; while the image quality analysers include the Blind/Reference-less Image Spatial QUality Evaluator (BRISQUE), Natural Image Quality Evaluator (NIQE) index and Reblurring based Peak Signal to Noise Ratio (RPSNR) measure. BRISQUE, NIQE and spectral kurtosis, are introduced for the first time as deblurring measures for BID. RPSNR is a novel full reference yet blind IQM designed and used in this research work. Experiments were conducted on different image datasets and real life blurred images. Optimization of the BID schemes has been achieved using a gradient descent based scheme and a Genetic Algorithm (GA). Quantitative results based on full-reference and non-reference IQMs, present BRISQUE as a robust and computationally efficient blind feedback quality measure. Also, parametric and arbitrarily shaped (non-parametric or generic) PSFs were treated for the blind deconvolution of images. The parametric forms of PSF include uniform Gaussian, motion and out-of-focus blur. The arbitrarily shaped PSFs comprise blurs that have a much more complex blur shape which cannot be easily modelled in the parametric form. A novel scheme for arbitrarily shaped PSF estimation and blind deblurring has been designed, implemented and tested on artificial and real life blurred images. The scheme provides a unified base for the estimation of both parametric and arbitrarily shaped PSFs with the BRISQUE quality measure in conjunction with a GA. Full-reference and non-reference IQMs have been utilised to gauge the quality of deblurred images for the BID schemes. In the real BID case, only non-reference IQMs can be employed due to the unavailability of the reference high quality image. Quantitative results of these images depict the restoration ability of the BID scheme. The significance of the research work lies in the BID scheme‘s ability to handle parametric and arbitrarily shaped PSFs using a single algorithm, for single-shot blurred images, with enhanced optimization through the gradient descent scheme and GA in conjunction with multiple feedback IQMs.

Low-field magnetic resonance imaging (MRI) has recently been integrated with radiation therapy systems to provide image guidance for daily cancer radiation treatments. The main benefit of the low-field strength is minimal electron return effects. The main disadvantage of low-field strength is increased image noise compared to diagnostic MRIs conducted at 1.5 T or higher. The increased image noise affects both the discernibility of soft tissues and the accuracy of further image processing tasks for both clinical and research applications, such as tumor tracking, feature analysis, image segmentation, and image registration. An innovative method, adaptive anatomical preservation optimal denoising (AAPOD), was developed for optimal image denoising, i. e., to maximally reduce noise while preserving the tissue boundaries. AAPOD employs a series of adaptive nonlocal mean (ANLM) denoising trials with increasing denoising filter strength (i. e., the block similarity filtering parameter in the ANLM algorithm), and then detects the tissue boundary losses on the differences of sequentially denoised images using a zero-crossing edge detection method. The optimal denoising filter strength per voxel is determined by identifying the denoising filter strength value at which boundary losses start to appear around the voxel. The final denoising result is generated by applying the ANLM denoising method with the optimal per-voxel denoising filter strengths. The experimental results demonstrated that AAPOD was capable of reducing noise adaptively and optimally while avoiding tissue boundary losses. AAPOD is useful for improving the quality of MRIs with low-contrast-to-noise ratios and could be applied to other medical imaging modalities, e.g., computed tomography. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

This work applies sparse representations and nonlinear image processing to two inverse imaging problems. The first problem involves image restoration, where the aim is to reconstruct an unknown high-quality image from a low-quality observed image. Sparse representations of images have drawn a considerable amount of interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. The standard sparse representation, however, does not consider the intrinsic geometric structure present in the data, thereby leading to sub-optimal results. Using the concept that a signal is block sparse in a given basis —i.e., the non-zero elements occur in clusters of varying sizes — we present a novel and efficient algorithm for learning a sparse representation of natural images, called graph regularized block sparse dictionary (GRBSD) learning. We apply the proposed method towards two image restoration applications: 1) single-Image super-resolution, where we propose a local regression model that uses learned dictionaries from the GRBSD algorithm for super-resolving a low-resolution image without any external training images, and 2) image inpainting, where we use GRBSD algorithm to learn a multiscale dictionary to generate visually plausible pixels to fill missing regions in an image. Experimental results validate the performance of the GRBSD learning algorithm for single-image super-resolution and image inpainting applications. The second problem addressed in this work involves image enhancement for detection and segmentation of objects in images. We exploit the concept that even though data from various imaging modalities have high dimensionality, the data is sufficiently well described using low-dimensional geometrical structures. To facilitate the extraction of objects having such structure, we have developed general structure enhancement methods that can be used to detect and segment various curvilinear structures in images across different applications. We use the proposed method to detect and segment objects of different size and shape in three applications: 1) segmentation of lamina cribrosa microstructure in the eye from second-harmonic generation microscopy images, 2) detection and segmentation of primary cilia in confocal microscopy images, and 3) detection and segmentation of vehicles in wide-area aerial imagery. Quantitative and qualitative results show that the proposed methods provide improved detection and segmentation accuracy and computational efficiency compared to other recent algorithms.

<p>Motion blur is a form of image quality degradation, showing as content in the image smearing and not looking sharp. It is usually seen in photography due to relative motion between the camera and the scene (either camera moves or objects in the scene move). It is also seen in human vision systems, primarily on digital displays.</p> <p><br></p> <p>It is often desired to remove motion blurriness from images. Numerous works have been put into reducing motion blur <em>after</em> the image has been formed, e.g., for camera-captured ones. Unlike post-processing methods, we take the approach to prevent/minimize motion blur for both human and camera observation by pre-processing the source image. The pre-processed images are supposed to look sharp upon blurring. Note that, only pre-processing methods can deal with human-observed blurriness since the imagery can't be modified after it is formed on the retina.</p> <p><br></p> <p>Pre-processing methods face more fundamental challenges than post-processing ones. A problem inherent to such methods is the appearance of ringing artifacts which are intensity oscillations reducing the quality of the observed image. We found that these ringing artifacts have a fundamental cause rooted in the blur kernel. The blur kernel usually have very low amplitudes in some frequencies, significantly attenuating the signal intensity in these frequencies when it convolves an image. Pre-processing methods can usually reconstruct the targeted image to the observer but inevitably lose energy in those frequencies, appearing as artifacts. To address the artifact issue, we present a few approaches: (a) aligning the image content and the kernel in the frequency domain, and (b) redistributing their intensity variations elsewhere in the image. We demonstrate the effectiveness of our method in a working prototype, in simulation, and with a user study.</p>

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In the decades that followed World War II, tuition at American colleges was well within the financial reach of most families. Since 1980, however, it has grown more expensive to attend both public and private colleges, as tuition has surged at a rate that has far outpaced inflation. At the same time, the economic and lifestyle disparities between those who earn four-year degrees and those who do not have reached record levels. As a result, students have to go to college in order to have a realistic shot at prosperity, but must borrow significantly in order to afford the cost of attendance. Colleges are aware that whenever the subject of increased tuition comes up, be it a proposed increase or an official one, it is a threat to their image and is likely to be viewed as offensive by students, who are already straining from the high cost of college. Thus, colleges employ a range of image restoration theory strategies at all phases of the conflict management life cycle, in order to restore, repair, and protect their images. While the rhetorical strategies taken by colleges may be given a great deal of thought by college spokespersons, they are not always strategically appropriate. This thesis uses mental accounting to extend image restoration theory, and offers rhetorical strategies that colleges may consider in order to minimize the threat to their images posed by increased tuition. / Master of Arts

Syftet med studien var att undersöka hur Parken Zoo använde sig av Facebook för att kommunicera med sina intressenter under en kris som drabbade organisationen hösten 2012. Med hjälp av en kvalitativ textanalys studerade vi de Facebookinlägg som Parken Zoo hade publicerat under krisen. Därefter genomförde vi en kvalitativ intervju med marknadschefen på Parken Zoo, som var ansvarig för kommunikationen på Facebook under krisen. Med hjälp av ett analysverktyg baserat på situational crisis communication theory och image restoration theory kunde vi avgöra vilka försvarsstrategier Parken Zoo hade använt i Facebookinläggen. Resultatet visade att Parken Zoo omedvetet hade använt många olika försvarsstrategier, men att tre tydliga budskap förmedlades i Facebookinläggen. Resultatet visade också att Parken Zoos syfte var att använda Facebook som ett dialogverktyg, men att de i själva verket använde Facebook för att sprida enkelriktad kommunikation. En slutsats vi kunde dra från studen var att det är viktigt för organisationer att vårda sina kundrelationer genom att bemöta intressenternas frågor och kommentarer på sin Facebooksida under en kris. En annan slutsats var att det är viktigt för organisationer att ha en god krisberedskap och att inkludera Facebook i den. / The purpose of this study was to examine how Parken Zoo used Facebook to communicatate with their stakeholders during a crisis 2012. Using a qualitative textual analysis, we examined Parken Zoo’s Face- book posts during the crisis. Next, we conducted a qualitative interview with the marketing manager at Parken Zoo, who was responsible for the organization’s communication on Facebook during the crisis. Using an analysis tool based on situational crisis communication theory and image restoration theory, we assessed which crisis response strategies Parken Zoo had used in their Facebook posts. The results showed that Parken Zoo subconsciously had used many different crisis response strategies, but that three clear messages were conveyed in the Facebook posts. The results also showed that Parken Zoo’s aim was to use Facebook as a dialogue tool, but in fact they used it for one-way communication. One conclusion from this study was that it is important for organizations to care for their relationship with the stakeholders by responding to their questions and comments on their Facebook page during a crisis. Another conclusion was that it is important for organizations to develop a proactive crisis management portfolio and to include Facebook in it.