Applications of imaging and image processing become a part of our daily life and find their crucial way in real-world areas. Accordingly, the corresponding techniques get more and more complicated. Many tasks are recognizable for a image processing chain, such as, filtering, color balancing, enhancement, segmentation, and post processing. Generally speaking, all of the image processing techniques need a control parameter setting. The better these parameters are set the better results can be achieved. Usually, these parameters are real numbers so search space is really large and brute-force searching is impossible or at least very time consuming. Therefore, the optimal setting of the parameters is an essential requirement to obtain desirable results. Obviously, we are faced with an optimization problem, which its complexity depends on the number of the parameters to be optimized and correlation among them. By reviewing the optimization methods, it can be understood that metaheuristic algorithms are the best candidates for these kind of problems. Metaheuristic algorithms are iterative approaches which can search very complex large spaces to come up with an optimal or close to optimal solution(s). They are able to solve black-box global optimization problems which are not solvable by classic mathematical methods. The first part of this thesis optimizes the control parameters for an eye-illusion, image enhancement, and image thresholding tasks by using an interactive evolutionary optimization approach. Eye illusion and image enhancement are subjective human perception-based issues, so, there is no proposed analytical fitness function for them. Their optimization is only possible through interactive methods. The second part is about setting of active contour (snake) parameters. The performance of active contours (snakes) is sensitive to its eight correlated control parameters which makes the parameter setting problem complex to solve. In this work, wehave tried to set the parameters to their optimal values by using a sample segmented image provided by an expert. As our case studies, we have used breast ultrasound, prostate ultrasound, and lung X-ray medical images. The proposed schemes are general enough to be investigated with other optimization methods and also image processing tasks. The achieved experimental results are promising for both directions, namely, interactive-based image processing and sample-based medical image segmentation. / UOIT

A fully automatic de-weathering system to increase the visibility/stability in surveillance applications during bad weather has been developed. Rain, snow and haze during daylight are handled in real-time performance with acceleration from CUDA implemented algorithms. Video from fixed cameras is processed on a PC with no need of special hardware except an NVidia GPU. The system does not use any background model and does not require any precalibration. Increase in contrast is obtained in all haze/rain/snow-cases while the system lags the maximum of one frame during rain or snow removal. De-hazing can be obtained for any distance to simplify tracking or other operating algorithms on a surveillance system.

Researchers analyze images in areas such as geology, bat cardiovascular systems and art studies to verify their observations. Some images are hard to study as their details are not vivid; hence there is a need to enhance these images to facilitate their study while preserving their contents. This study is aimed at assisting the researchers in the Cardiovascular Systems Dynamic Laboratory at Texas A&M University by evaluating the importance of Image Enhancement (IE) for improving visibility of features. For this study the images were collected and manipulated using various IE techniques and were shown to the novice researchers who were asked to perform three different tasks. These tasks were representative of the research work conducted in the lab. The techniques that were selected aimed at reducing the problems that are usually associated with data obtained from microscopic feeds. A customized application was developed to expedite and automate the study. The results indicated that the researchers did not immmensely benefit from the improved visualization for easy tasks. However, their performance improved for tasks that required more practice and skill. Our approach contributes towards designing an effective training program for novice researchers in the lab. Moreover, it is promising for similar research in different fields of study.

Degradation in visibility is often introduced to images captured in poor weather conditions, such as fog or haze. In this paper, a fusion-based transmission estimation method is introduced to adaptively combine two different transmission models. Specifically, the new fusion weighting scheme and the atmospheric light computed from the Gaussian-based dark channel method improves the estimation of the locations of the light sources. To reduce the flickering effect introduced during the process of frame-based dehazing, a flicker-free module is formulated to alleviate the impacts. The system assessments show this approach is capable of superior defogging and dehazing performance, compared to the state-of-the-art methods, both quantitatively and qualitatively

This thesis is divided into two general sections. The first deals with the theory of digital magnetic recording and surveys the more common techniques used in digital magnetic tape recording. A new recording format is described which leads to a greater data rate at normal recording densities. The design and implementation of a recording system using this technique is described. The second section outlines the design of a portable digital recording system compatible with the peripheral unit described in section 1. The application of this system to the acquisition of data from a radioisotope scanner used in nuclear medicine is described and some basic results obtained from the system are demonstrated. Fundamental work towards image enhancement is outlined. / Thesis / Master of Engineering (ME)

<p>Technologies in image acquisition have developed and often provide image volumes in more than two dimensions. Computer tomography and magnet resonance imaging provide image volumes in three spatial dimensions. The image enhancement methods have developed as well and in this thesis work 3D image enhancement with filter networks is evaluated.</p><p>The aims of this work are; to find a method which makes the initial parameter settings in the 3D image enhancement processing easier, to compare 2D and 3D processed image volumes visualized with different visualization techniques and to give an illustration of the benefits with 3D image enhancement processing visualized using these techniques.</p><p>The results of this work are;</p><p>1. a parameter setting tool that makes the initial parameter setting much easier and</p><p>2. an evaluation of 3D image enhancement with filter networks that shows a significant enhanced image quality in 3D processed image volumes with a high noise level compared to the 2D processed volumes. These results are shown in slices, MIP and volume rendering. The differences are even more pronounced if the volume is presented in a different projection than the volume is 2D processed in.</p>

Technologies in image acquisition have developed and often provide image volumes in more than two dimensions. Computer tomography and magnet resonance imaging provide image volumes in three spatial dimensions. The image enhancement methods have developed as well and in this thesis work 3D image enhancement with filter networks is evaluated. The aims of this work are; to find a method which makes the initial parameter settings in the 3D image enhancement processing easier, to compare 2D and 3D processed image volumes visualized with different visualization techniques and to give an illustration of the benefits with 3D image enhancement processing visualized using these techniques. The results of this work are; 1. a parameter setting tool that makes the initial parameter setting much easier and 2. an evaluation of 3D image enhancement with filter networks that shows a significant enhanced image quality in 3D processed image volumes with a high noise level compared to the 2D processed volumes. These results are shown in slices, MIP and volume rendering. The differences are even more pronounced if the volume is presented in a different projection than the volume is 2D processed in.

Diss. (sammanfattning) Linköping : Univ., 2001. / Härtill 5 uppsatser.

No description available.

No description available.