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This work extends the Bussgang blind equalization algorithm to the multichannel case with application to image deconvolution problems. We address the restoration of images with poor spatial correlation as well as strongly correlated (natural) images. The spatial nonlinearity employed in the final estimation step of the Bussgang algorithm is developed according to the minimum mean square error criterion in the case of spatially uncorrelated images. For spatially correlated images, the nonlinearity design is rather conducted using a particular wavelet decomposition that, detecting lines, edges, and higher order structures, carries out a task analogous to those of the (preattentive) stage of the human visual system. Experimental results pertaining to restoration of motion blurred text images, out-of-focus spiky images, and blurred natural images are reported.

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Abstract-We experimentally characterize the compression efficiency of Multiview Video Coding (MVC) techniques in Wireless Multimedia Sensor network (WMSN) composed of multiple video cameras with possibly overlapping field of views. We derive an empirical model that predicts the compression efficiency as a function of the common sensed area (CSA) between different camera views. We show that the CSA depends not only on geometrical relationships among the relative positions of different cameras, but also on several object-related phenomena, e.g., occlusions and motion, and on low-level phenomena such as variations in illumination. We then apply the model to a WMSN, where we create clusters based on the CSA as estimated by exchanging local data. Based on this estimates, we form clusters and measure the resulting transmission rate. Numerical simulation results show that building clusters based on a CSA criterion can bring significant performance gains in terms of bandwidth efficiency. The herein presented promising results pave the way for clustering optimization taking into account different networks constraints and conditions.

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