University of California, Riverside

Department of Electrical and Computer Engineering



Image Reconstruction in Coherent Imaging: From Statics to Dynamics


Image Reconstruction in Coherent Imaging: From Statics to Dynamics
 
Oleg Michailovich
Georgia Institute of Technology, Atlanta, Georgia

Date: Monday, March 6, 2006
Time: 11:00 am
Location: Bourns Hall A265

This talk addresses the problem of image reconstruction in coherent imaging which is considered to be a key imaging modality including many microwave, optical, and acoustic sensing applications. In all these cases, the quality of acquired images is often reduced due to a number of destructive factors. The finiteness of the bandwidth of transmitted waveforms as well as the finiteness of used apertures deteriorates the image resolution. Moreover, the interference between back-scattered signals, received by the imager, causes the images to contain an extremely complex pattern that generally demonstrates no obvious relationship to the properties of the given objects. This pattern, known as speckle noise, tends to obscure and mask important features, thereby necessitating the application of an image reconstruction procedure. In the class of signal processing solutions to the problem of image restoration, a multitude of possible approaches are possible. All these restoration methods may be separated into two groups, which exploit conceptually different models of the image formation process, which, in fact, result from different concepts of the nature of a "true image". While the methods of the first group attack the problem of resolution limitations by means of deconvolution techniques, those of the second group improve the image quality through rejecting the speckle noise, aiming to produce piecewise smooth images. Since the reconstructions produced by these two approaches have quite different properties and recover different types of useful information hidden in the original images, the approaches have long been regarded as being complementary. At the same time, there seems to be some agreement for the utility of equipping standard scanners with both types of reconstruction methodologies, as this would allow the observer to decide which image representation format is more appropriate for a specific task at hand. In this talk, this concept is further developed through demonstrating that reconstructions carried out via deconvolution and de-speckling can be integrated in a general purpose post-processing system, in which these methods "cooperate" in a computationally efficient manner, rather being applied separately. Moreover, besides being computationally efficient, the generalized system is shown to have advantage of producing de-speckled images, whose quality is superior to that of the images obtained by means of "stand alone" de-speckling. Additionally, the applicability of the above concept to the problem of simultaneous enhancement and predictive target tracking for dynamic image sequences is demonstrated. Specifically, we introduce dynamic de-noising as a tool for simultaneously enhancing and tracking image sequences. This method employs previous observations of a dynamic scene to be recovered in order to restore its current noisy observation. The mechanism allowing the fusion of the information within successive image frames is Bayesian estimation, while transferring the useful information along the image frames is governed by a Kalman filter, which is used for prediction and estimation of the scene dynamics. Thus, in this methodology, the processes of target tracking and de-noising appear in an interlacing manner. The results demonstrated in this presentation indicate a number of advantages of the proposed dynamic de-noising over "static" approaches, where the images are recovered separately. A potential application of the proposed processing to cardiac elastography is discussed as well.

About the speaker:

Oleg V. Michailovich is a research scientist in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. He earned his B.S. and M.S. degrees in Electrical Engineering (magna cum laude) from the Saratov State University (Russia) in 1994. In 1997, he immigrated to Israel, where he joined the Department of Biomedical Engineering at the Technion - Israeli Institute of Technology in the spring of 1998 as a graduate student. He received his M.S. and Ph.D. degrees in Biomedical Engineering from the Technion in 2000 and 2003, respectively. In 2001, 2002, and 2003, he was given the excellence award for outstanding grades and research achievements of the Israel Planning and Budgeting Committee of the Council for Higher Education. His dissertation was dedicated to developing efficient reconstruction methods for medical ultrasound imaging. This work resulted in a number of publications and patents, and it underlay a fruitful collaboration between the Technion and GE Ultrasound.
In 2003, Oleg Michailovich moved to Atlanta, GA where he joined the School of Electrical and Computer Engineering at the Georgia Institute of Technology as a post-doctoral fellow. In 2004, he was promoted to research scientist. His current research interests include solution of inverse problems in signals and image processing, multiresolution analysis and representation of signals and images, image segmentation, fusion and registration, motion estimation and tracking, as well as the approximation theory and its applications in signal and image processing.

Oleg Michailovich is a member of the Institute of Electrical and Electronic Engineers and of the Israeli Society of Biomedical Engineering. For the last three years, he has been serving as a reviewer for such professional journals as IEEE Transactions on Signal Processing, Image Processing, Medical Imaging, and UFFC.
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