Low-resolution Images (low-resolution + image)

Distribution by Scientific Domains


Selected Abstracts


MAP fusion method for superresolution of images with locally varying pixel quality

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 4 2008
Kio Kim
Abstract Superresolution is a procedure that produces a high-resolution image from a set of low-resolution images. Many of superresolution techniques are designed for optical cameras, which produce pixel values of well-defined uncertainty, while there are still various imaging modalities for which the uncertainty of the images is difficult to control. To construct a superresolution image from low-resolution images with varying uncertainty, one needs to keep track of the uncertainty values in addition to the pixel values. In this paper, we develop a probabilistic approach to superresolution to address the problem of varying uncertainty. As direct computation of the analytic solution for the superresolution problem is difficult, we suggest a novel algorithm for computing the approximate solution. As this algorithm is a noniterative method based on Kalman filter-like recursion relations, there is a potential for real-time implementation of the algorithm. To show the efficiency of our method, we apply this algorithm to a video sequence acquired by a forward looking sonar system. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 242,250, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). [source]


Image-based coronary tracking and beat-to-beat motion compensation: Feasibility for improving coronary MR angiography

MAGNETIC RESONANCE IN MEDICINE, Issue 3 2008
Maneesh Dewan
Abstract A method to reduce the effect of motion variability in MRI of the coronary arteries is proposed. It involves acquiring real-time low-resolution images in specific orthogonal orientations, extracting coronary motion from these images, and then using this motion information to guide high-resolution MR image acquisition on a beat-to-beat basis. The present study establishes the feasibility and efficacy of the proposed approach using human motion data in an offline implementation, prior to future online implementation on an MRI scanner. To track the coronary arteries in low-resolution real-time MR images in an accurate manner, a tracking approach is presented and validated. The tracking algorithm was run on real-time images acquired at 15,20 frames per second in four-chamber, short-axis, and coronal views in five volunteers. The systolic and diastolic periods in the cardiac cycles, computed from the extracted motion information, had significant variability during the short time periods typical of cardiac MRI. It is also demonstrated through simulation analysis using human tracked coronary motion data that accounting for this cardiac variability by adaptively changing the trigger delay for acquisition on a beat-to-beat basis improves overall motion compensation and hence MR image quality evaluated in terms of SNR and CNR values. Magn Reson Med 60:604,615, 2008. © 2008 Wiley-Liss, Inc. [source]


Achieving super-resolution X-ray imaging with mobile C-arm devices

THE INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY, Issue 3 2009
C. Bodensteiner
Abstract Background The term super-resolution refers to the process of combining a set of low-resolution images into a high-resolution image using image processing methods. This work is concerned with the reconstruction of high-resolution X-ray images. Specifically, we address the problem of acquiring X-ray images from multiple, very close view points. Methods We propose to use a novel experimental robotic C-arm device to create high-resolution X-ray images. For this purpose, we suggest different strategies for acquiring multiple low-resolution images, and we provide the steps to achieve acquisition-error compensation. Compared to visible light images, X-ray images have the particularity that parallax effects render super-resolution very difficult. Using the acquired multi-frame data, we evaluate recent well-known super-resolution reconstruction algorithms. The same algorithms are evaluated based on synthetic 3D phantom data and real X-ray images. Results In experiments with both synthetic and real projection data, we successfully reconstruct up to four times higher-resolution images. These images reveal structures and details which are not perceivable in the low-resolution images. Conclusions The advantage of super-resolution techniques for X-ray is the potential reduction of radiation dose for patients and medical personnel. Potential medical applications include the diagnosis of early-stage osteoporosis and the detection of very small calcifications. Copyright © 2009 John Wiley & Sons, Ltd. [source]