This letter introduces a lossless reversible data hiding method. The original image is recovered without any distortion from the marked image following the extraction of the hidden data. The main algorithm is based on the relocation of zeros (or minima) and peaks of the histograms of the blocks of the original image to embed the data. It leads to the modification of the grey values of some pixels. It can embed more data than many of the existing reversible data hiding algorithms.The PSNR of the proposed method is better than many of the existing reversible data hiding techniques. Some of the huge experimental results are presented to prove its validity.

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In this study, we have introduced an accurate retinal images registration method using affine moment invariants (AMI's) which are the shape descriptors. First, some closed-boundary regions are extracted in both reference and sensed images. Then, AMI's are computed for each of those regions. The centers of gravity of three pairs of regions which have the minimum of distances are selected as the control points. The region matching is performed by the distance measurements of AMI's. The evaluation of region matching is performed by comparing the angles of three triangles which are built on these three-point pairs in reference and sensed images. The parameters of affine transform can be computed using these three pairs of control points. The proposed algorithm is applied on the valid DRIVE database. In general (for the case, each sensed image is produced by rotating, scaling, and translating the reference image with different angles, scale factors, and translation factors), the success rate and accuracy is 95 and 96 %, respectively.

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a b s t r a c tDiffusion coefficient has an important role in the performance of partial differential equation (PDE) based image denoising techniques. Commonly, the classical Perona-Malik (PM) diffusion coefficient is widely used in PDE-based noise removal algorithms. In this paper, PM diffusion coefficient is analyzed regarding to its flux. Based on the analysis, PM flux for regions where the gradient magnitude is higher than smoothing threshold may lead to undesirable blurring effect and edge displacement. To address these issues, the image is divided into three segments based on the gradient magnitude: regions where the gradient is lower than the smoothing threshold, regions where the gradient is between the smoothing threshold and inflection point of flux, and regions where the gradient magnitude is higher than inflection point. We define the conditions that should be considered in these three segments. Then, a diffusion coefficient, satisfying all these conditions, is computed. Experimental results confirm the performance of the proposed method with regard to peak signal-to-noise ratio (PSNR), mean structural similarity (MSSIM), universal quality index (UQI), visual information fidelity (VIF), feature similarity (FSIM), information content weighted SSIM (IW-SSIM) and visual quality.

Abstract:Since histogram-based methods are formed by some simple differences, they are very desirable for deinterleaving. However, their main imperfection concerns recognizing complex pulse repetition interval (PRI) patterns like jittered and staggered ones. In this paper, we present new thresholds to detect jittered and staggered PRI from histogram-based methods even in complex circumstances such as noisy time of arrival (TOA), complex PRI patterns, and large missing pulses. Simulation results demonstrate that our method can detect and extract constant, jittered, and staggered PRI correctly. Moreover, the method is proved to be considerably robust and reliable at a missing pulses rate up to 30%.