Abstract

The new signal acquisition methodology of compressive sensing (CS) has generated a great deal of enthusiasm in the signal processing community. Researchers are excited by the prospect of achieving compact signal representation by reduced random sampling of CS rather than the prevailing practice of oversampling followed by (typically transform-based) compression. A challenge for CS is to find a space in which the signal is sparse and hence recoverable faithfully. Given the nonstationarity of many natural signals such as images, the sparse space is varying in time or spatial domain. As such, CS recovery should be conducted in locally adaptive, signal-dependent spaces to counter the fact that the CS measurements are global and irrespective of signal structures. On the contrary existing CS reconstruction methods use a fixed set of bases (e.g., wavelets, DCT, and gradient spaces) for the entirety of a signal. To rectify this problem we propose a new framework for model-guided adaptive recovery of compressive sensing (MARX), and show how a 2D piecewise autoregressive model can be integrated into the MARX framework to make CS recovery adaptive to spatially varying second order statistics of an image.  In addition, MARX offers a powerful mechanism of characterizing and exploiting structured sparsities of natural images, greatly restricting the CS solution space.  Simulation results over a wide range of natural images show that the proposed MARX technique can improve the reconstruction quality of existing CS methods by 2~7 dB.

We also propose a new image acquisition and recovery strategy of hybrid sensing (HS) that combines random sampling of compressive sensing (CS) and uniform down sampling.  HS lets the two sampling schemes complement each other so that one can have the best of both worlds: signal-independent sparse sampling that is the hallmark of CS, and locally adaptive signal reconstruction that is afforded by uniform sampling.  We suggest a few important applications of HS in image acquisition and communication, such as as multispectral imaging, multiple description image coding, multiview video, and ultra-high throughput imaging (e.g., functional medical imaging).

 Biography

Xiaolin Wu received his B.Sc in 1982 from Wuhan University, China, and Ph.D in 1988 from University of Calgary, Canada, both in computer science. He is currently a professor at the Department of Electrical & Computer Engineering, McMaster University, Canada, where he holds the NSERC-DALSA research chair in digital cinema. Prof. Wu is the recipient of the 1998 UWO distinguished research professorship, Canada, the 2000 Monsteds Fellowship, Demark, and a 2003 Nokia visiting research fellowship, Finland.  Prof. Wu’s research interests include source coding, multimedia computing and communications, joint source-channel coding, and image processing.  He has published over 180 research papers and holds two patents in these fields. He is an associated editor for both IEEE Transactions on Image Processing and IEEE Transactions on Multimedia.

联系人：季向阳

联系电话：62788613-815

E-Mail:  xyji@tsinghua.edu.cn