Maryam Amirmazlaghani

Bio: Maryam Amirmazlaghani is an academic researcher from Amirkabir University of Technology. The author has contributed to research in topics: Statistical model & Wavelet transform. The author has an hindex of 11, co-authored 41 publications receiving 387 citations.

Speckle Suppression in SAR Images Using the 2-D GARCH Model

Abstract: A novel Bayesian-based speckle suppression method for Synthetic Aperture Radar ( SAR) images is presented that preserves the structural features and textural information of the scene. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the wavelet coefficients of SAR images have significantly non-Gaussian statistics that are best described by the 2-D GARCH model. By using the 2-D GARCH model on the wavelet coefficients, we are capable of taking into account important characteristics of wavelet coefficients, such as heavy tailed marginal distribution and the dependencies between the coefficients. Furthermore, we use a maximum a posteriori (MAP) estimator for estimating the clean image wavelet coefficients. Finally, we compare our proposed method with various speckle suppression methods applied on synthetic and actual SAR images and we verify the performance improvement in utilizing the new strategy.

Two Novel Bayesian Multiscale Approaches for Speckle Suppression in SAR Images

Abstract: Speckle suppression is a prerequisite for many synthetic aperture radar (SAR) image-processing tasks. Previously, we introduced a Bayesian-based speckle-suppression method that employed the 2-D generalized autoregressive conditional heteroscedasticity (2D-GARCH) model for wavelet coefficients of log-transformed SAR images. Based on this method, we propose two new Bayesian speckle-suppression approaches in this paper. In the first approach, we introduce a new heteroscedastic model, i.e., the 2D-GARCH Mixture (2D-GARCH-M) model, as an extension of the 2D-GARCH model. This new model can capture the characteristics of wavelet coefficients. Also, the 2D-GARCH-M model introduces additional flexibility in the model formulation in comparison with the 2D-GARCH model, which results in better characterization of SAR image subbands and improved restoration in noisy environments. Then, we design a Bayesian estimator for estimating the clean-image wavelet coefficients based on 2D-GARCH-M modeling. In the second approach, the logarithm of an image is analyzed by means of the curvelet transform instead of wavelet transform. Then, we study the statistical properties of curvelet coefficients, and we demonstrate that the 2D-GARCH model can capture the characteristics of curvelet coefficients, such as heavy tailed marginal distribution, and the dependences among them. Consequently, under the 2D-GARCH model, we design a Bayesian estimator for estimating the clean-image curvelet coefficients. Finally, we compare these methods with other denoising methods applied on artificially speckled and actual SAR images, and we verify the performance improvement in utilizing the new strategies.

A novel robust scaling image watermarking scheme based on Gaussian Mixture Model

Abstract: We propose a wavelet domain scaling watermarking method.We design an ML detector and analyze its performance analytically.We use L-curve method to find tradeoff between imperceptibility and robustness.Experimental results demonstrate the high performance of the proposed method. In this paper, we propose a novel scaling watermarking scheme in which the watermark is embedded in the low-frequency wavelet coefficients to achieve improved robustness. We demonstrate that these coefficients have significantly non-Gaussian statistics that are efficiently described by Gaussian Mixture Model (GMM). By modeling the coefficients using the GMM, we calculate the distribution of watermarked noisy coefficients analytically and we design a Maximum Likelihood (ML) watermark detector using channel side information. Also, we extend the proposed watermarking scheme to a blind version. Consequently, since the efficiency of the proposed method is dependent on the good selection of the scaling factor, we propose L-curve method to find the tradeoff between the imperceptibility and robustness of the watermarked data. Experimental results demonstrate the high efficiency of the proposed scheme and the performance improvement in utilizing the new strategy in comparison with the some recently proposed techniques.

ReDMark: Framework for Residual Diffusion Watermarking based on Deep Networks

Abstract: Due to the rapid growth of machine learning tools and specifically deep networks in various computer vision and image processing areas, applications of Convolutional Neural Networks for watermarking have recently emerged. In this paper, we propose a deep end-to-end diffusion watermarking framework (ReDMark) which can learn a new watermarking algorithm in any desired transform space. The framework is composed of two Fully Convolutional Neural Networks with residual structure which handle embedding and extraction operations in real-time. The whole deep network is trained end-to-end to conduct a blind secure watermarking. The proposed framework simulates various attacks as a differentiable network layer to facilitate end-to-end training. The watermark data is diffused in a relatively wide area of the image to enhance security and robustness of the algorithm. Comparative results versus recent state-of-the-art researches highlight the superiority of the proposed framework in terms of imperceptibility, robustness and speed. The source codes of the proposed framework are publicly available at Github 1 .

Bayesian Inverse Synthetic Aperture Radar Imaging

Abstract: In this letter, a novel algorithm of inverse synthetic aperture radar (ISAR) imaging based on Bayesian estimation is proposed, wherein the ISAR imaging joint with phase adjustment is mathematically transferred into signal reconstruction via maximum a posteriori estimation. In the scheme, phase errors are treated as model errors and are overcome in the sparsity-driven optimization regardless of the formats, while data-driven estimation of the statistical parameters for both noise and target is developed, which guarantees the high precision of image generation. Meanwhile, the fast Fourier transform is utilized to implement the solution to image formation, promoting its efficiency effectively. Due to the high denoising capability of the proposed algorithm, high-quality image also could be achieved even under strong noise. The experimental results using simulated and measured data confirm the validity.