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Some methods developed in reversible data hiding (RDH) make use of prediction for data embedding for original pixel estimation. Predicators may also be exploited in RDH in encrypted image (RDHEI); this has become a research interest in recent years because of the development of cloud computing and a need for content owner privacy. This paper presents a high-capacity reversible data hiding in encrypted image (RDHEI) that employs local difference predictor. In this algorithm, an image is divided into non-overlapping blocks. In each block, the central pixel of the block is considered as the leader pixel and others as follower ones. The prediction errors between the intensity of the follower pixels and leader one are calculated using local difference predictor and analyzed to determine a label for block embedding capacity. This label indicates the amount of data that can be embedded in a block after encryption. Using this pre-processing for all blocks, we vacate rooms before the encryption of the original image to achieve high embedding capacity. Also, using these labels, embedded data is extracted and the original image is losslessly reconstructed at the decoding phase. Comparing to existent RDHEI algorithms, not only embedding capacity is increased by the proposed algorithm, but also a perfect reconstruction of the original image is realized by content owner without having data hider key. Experimental results confirm that the proposed algorithm outperforms state of the art RDHEI methods. [ABSTRACT FROM AUTHOR]

This paper presents an analytical method to estimate the location and number of actual small targets in 2Dinverse scattering problems. This method is motivated from the exact maximum likelihood estimation of signal parameters in white Gaussian noise for the linear data model. In the first stage, the method uses the MUSIC algorithm to acquire all possible target locations and in the next stage, it employs an analytical formula thatworks as a spatial filter to determine which target locations are associated to the actual ones. The ability of the method is examined for both the Born and multiple scattering cases and for the cases of well-resolved and non-resolved targets. Many numerical simulations using both the coincident and non-coincident arrays demonstrate that the proposed method can detect the number of actual targets even in the case of very noisy data and when the targets are closely located. Using the experimental microwave data sets, we further show that this method is successful in specifying the number of small inclusions. [ABSTRACT FROM AUTHOR]

This article introduces a recursive formula, in which any approximate method can, iteratively, be incorporated to solve electromagnetic scattering problems. The use of extended Born approximation and quasi-analytical approximation in this formula results in two series (algorithms), whose convergence properties are discussed. Numerical examples are given to demonstrate merits and the range of applicability of the new series. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2425–2429, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22781 [ABSTRACT FROM AUTHOR]