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A watermarking method for color image content authentication with localization and recovery capability of the tampered areas is proposed, in which a halftone image is used as approximate version for the luminance information and the coefficients of the Discrete Cosine Transform (DCT) of the two chrominance channels are encoded and used as approximate version of color information. The luminance and color information are embedded into the lower sub-bands of the Integer Wavelet Transform (IWT) of the host image using the Quantization Index Modulation-Dither Modulation (QIM-DM). The luminance information is embedded into the LL sub-band, while the color information is embedded redundantly into the LH and HL sub-bands in order to increase the robustness against JPEG compression. Moreover, a Multilayer Perceptron (MLP) Neural Network is employed as inverse halftoning process to improve the recovered image quality. The experimental results demonstrate the effectiveness of the proposed method compared with previously proposed methods.

This paper proposes a new application based on fuzzy logic to search the optimal Step Size of the Normalized Least Mean Square (NLMS) algorithm for beamforming systems. The searching of the step size depends on the fuzzy inference results of an estimation of the final value of the cost function (J E ) instead of using its instantaneous value. The update of the step-size is performed outside of the adaptive algorithm and given it feedback by the fuzzy inference system; therefore the step-size is still fixed for the NLMS algorithm but variable for the complete searching scheme. Simulation experimental results show that a useful approximation of the optimal step-size can be obtained for different conditions of signal-to-noise plus interference ratios (SINR) and the minimization of the mean square error for the adaptive beamforming algorithm is also achieved.

Halftoning and inverse halftoning algorithms are very important image processing tools that have been widely used in digital printers, scanners, steganography and image authentication systems. Because such applications require obtaining high quality gray scale images from its halftoning versions, several inverse halftoning algorithms have been proposed during the last several years, which provide gray scale images with Peak Signal to Noise Ratio (PSNR) of about 25 to 28 dB. Although this may be enough for several applications, exist several other that require higher image quality. To this end, this paper proposes an inverse halftoning algorithm based on Upx atomic function and multilayer perceptron neural network. Experimental results show that proposed scheme provides gray scale images with PSNRs higher than 30dB independently of the method used to generate the halftone image.