Edge Detection and Compressive Sensing-Oriented Digital Image Block Watermarking Algorithm

In this paper, a robust algorithm of digital image watermarking based on discrete wavelet transform is introduced It uses blind watermarking technique. Digital image watermarking is one such technology that has been developed to protect digital images from illegal manipulations. In particular, digital image watermarking algorithms which are based on the discrete wavelet transform have been widely recognized to be more prevalent than others. This is due to the wavelets' excellent spatial localization, frequency spread, and multi-resolution characteristics, which are similar to the theoretical models of the human visual system.

Time delay estimation (TDE) is a hot research topic in wireless location technology. Compressed sensing (CS) theory has been widely applied to image reconstruction and direction of arrival estimation since it was proposed in 2004. The sparse model can be constructed in time domain for estimating the time delay by using the CS theory. The measurement matrix plays a crucial role in the processing of signal reconstruction which is the core problem of CS theory. Therefore the research in the measurement matrix has becomes a hotspot in recent years. The existing measurement matrix is mainly divided into two categories, i.e., random measurement matrix and deterministic measurement matrix. The performance of random measurement matrix has bottlenecks. Firstly, because of the redundant measurement matrix data, the generation and storage of the random number put forward a high requirement for hardware. Secondly the random matrix can only satisfy the restricted isometry property in a statistical sense. The research of the deterministic measurement matrix is of great value under this background. The parity check matrix of low density parity check (LDPC) code has good performance in CS theory. However, the method of randomly selecting non-zero element position has a certain probability to generate a measurement matrix with a short loop structure during generating LDPC code measurement matrix. The robustness of the reconstruction performance decreases with the increase of iteration times. A novel quasi-cyclic CS algorithm based on progressive edge-growth is constructed to estimate the time delay. The purpose of this article is to deal with the need to store a large number of data in existing measurement matrix during time delay, by using the CS theory. The algorithm presented here can achieve TDE in a high precision. First, the theoretical bridge between CS and the maximum likelihood decoding is established. And the design criterion of measurement matrix based on the LDPC code is derived. The sparse measurement matrix with quasi-cyclic structure is constructed by introducing the idea of progressive edge-growth. Finally, the orthogonal matching pursuit algorithm is used to estimate the time delay. Furthermore, the computational complexity of the algorithm and the data storage of the measurement matrix are analyzed theoretically. Simulations show that the correct reconstruction probability of the proposed approach is higher than those of the Gauss random matrix and random LDPC matrix under the same dimension. Compared with the random LDPC matrix, the proposed method can improve performance at the expense of less complexity under the condition of the same data storage.

The recently introduced Compressed Sensing (CS) theory explains how sparse or compressible signals can be reconstructed from far fewer samples than what was previously believed possible. The CS theory has attracted significant attention for applications such as Magnetic Resonance Imaging (MRI) where long acquisition times have been problematic. This is especially true for dynamic MRI applications where high spatio-temporal resolution is needed. For example, in cardiac cine MRI, it is desirable to acquire the whole cardiac volume within a single breath-hold in order to avoid artifacts due to respiratory motion. Conventional MRI techniques do not allow reconstruction of high resolution image sequences from such limited amount of data. Vaswani et al. recently proposed an extension of the CS framework to problems with partially known support (i.e. sparsity pattern). In their work, the problem of recursive reconstruction of time sequences of sparse signals was considered. Under the assumption that the support of the signal changes slowly over time, they proposed using the support of the previous frame as the "known" part of the support for the current frame. While this approach works well for image sequences with little or no motion, motion causes significant change in support between adjacent frames. In this paper, we illustrate how motion estimation and compensation techniques can be used to reconstruct more accurate estimates of support for image sequences with substantial motion (such as cardiac MRI). Experimental results using phantoms as well as real MRI data sets illustrate the improved performance of the proposed technique.

Due to the rise in many industrial environment applications involving distributed networked collaborative intelligent measurement systems, intelligent monitoring and multi-sensor information exchange of private information, digital image information security and hidden image authentication, tampering detection, correction, and automatic intelligent self-recovery have become critical concerns. Towards that end, there is a need for more secure, efficient, and robust-to-unauthorized-tampering digital image watermarking, information hiding and authentication schemes. In our prior work, we introduced non-blind watermarking schemes where the carrier and watermark images are available at the receiver's end. We also proposed a double-blind scheme where both the carrier and the watermark images are not available at the receiver's end, but at the expense of possible lower pixel tampering, authentication and automatic intelligent self-correction ability. This paper proposes a blind wavelet-based, high capacity, secure digital watermarking and information hiding scheme with higher tampering localization and authentication that nullifies the need to have the carrier image available at the receiving end. The proposed scheme also increases robustness to image authentication and automatic self-recovery and correction compared to our prior double-blind scheme. The scheme is high capacity and sustains hiding secret hidden and watermarking images of the same full size as an arbitrary-sized carrier image. One of the benefits of the blind watermarking scheme is that it allows usage of varying and arbitrary carrier images that are unknown and unavailable anywhere, including the receiver's end. This is to prevent hackers' recognition of a familiar carrier image and their potential unauthorized tampering. Here, in this paper, two grayscale images are inserted in a colored carrier image. Here the watermark image is used for authentication and tamper localization. The second image is used as ownership or encoded secret hidden message image. The Arnolds Transform is used to introduce added security to a hidden image. The proposed scheme uses an embedded correction matrices mechanism for the extracted hidden information self-recovery from various tampering and digital attacks by unauthorized hackers.

Recently, works are completely based on the internet, so authentication is required to protect the content of the data because owner never wants to degrade the quality of his data. Information hiding via digital watermarks for the multimedia data is the compatible way to provide the protection of data. Watermarks are imperceptible and it is predefined pattern inserting into multimedia data to protect or authenticity. The watermark indicates that data is containing a copyright or not. Many digital watermarking algorithms have been proposed in spatial and transform domain. The performance of the digital watermarking schemes is evaluated as tradeoffs between images embedded on it, robustness against attacks and embedding induced distortions. This work provides Digital image watermarking using Fractional Fourier transform via image compression for better results comparatively previous techniques of information hiding.

Security and authentication of the people information is effectively provided through various watermarking algorithms. Most of the conventional watermarking algorithms have their own drawbacks in maintaining the quality and security of the image against various attacks. In order to overcome the above stated issues, a watermarking technique based on optimization is required for preserving image quality. In this paper, singular value decomposition (SVD) based digital image watermarking (DIW) scheme is proposed in hybrid discrete cosine transform (DCT) and discrete wavelet transform (DWT) using optimization algorithms like particle swarm optimization (PSO) and Jaya algorithm. The watermark image (WI) is embedded into low frequency DWT sub-band of DCT of cover image. PSO and Jaya algorithms are applied to improve the security threats and quality by assessing the fitness function. The work involves in utilizing the feature points in embedding of watermarks and analyzing the impact of various types of attacks with respect to image quality. The performance parameters like peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM) and correlation coefficient of fitness function given by PSO and Jaya algorithm.

Compressive Sensing (CS) is a new method for sparse images reconstruction using incomplete measurements. In this study our goal is to reconstruct a High Resolution (HR), MR image from a single Low Resolution (LR) image. Our proposed method applies the CS theory to Super Resolution (SR) single Magnetic Resonance Imaging (MRI). We first use a LR image generated by applying a Gaussian filter on the original image (for k-space under-sampling) and then generate the HR image by using CS theory. The formulation of CS theory emphasizes on maximizing image sparsity on known sparse transform domain and minimizing fidelity. For satisfying sparsity, finite difference is applied as a sparsifying transform. We propose and compare the Non-Linear Conjugate Gradients (NLCG) and Split Bregman (SB) algorithms as two different image reconstructing methods in CS. The result images are compared with three types of images: Original image which is used as the input of experiments, low quality of original image and the image which is generated by Zero Filling (ZF) algorithm. The following measures are used for evaluation: SNR, PSNR, SSIM and MSE. Experiments show that the SB algorithm outperforms ZF and NLCG for reconstructing MR images.

The growth of network multimedia systems has expanded the demand for image copyright protection. Digital watermark is one approach which is often used for image copyright protection. However, there are limitations of robustness and confidentiality in the common digital watermarking algorithms to resist print-scan attacks. In this paper, a new method combining zero watermarking, blind watermarking and Arnold scrambling is proposed. The zero-watermarking algorithm in DCT low-frequency region avoids the limitation of watermarking capacity to a certain extent. And it has a certain degree of stability and adaptability as well as a strong anti-interference ability to the geometric attacks. In the intermediate-frequency region, the blind watermarking algorithm is used to embed the scrambling information of the watermark and it is robust. With such multilayer enciphering, the confidentiality of the watermark is improved. The whole method has made great progress in the resistance of the geometric attacks and confidentiality of watermark. It's proved that the method is an applicable method against print-scan attacks. It has a certain reference value to the study of digital image watermark.

The digital watermarking algorithm based on discrete Hopfield neural networks and singular value decomposition have been put forward in this paper. Currently, there are some shortcomings such as weak resistance on the attacks of geometric distortion and noise in some digital watermarking algorithm. Firstly, block-based singular value decomposition was applied for the original image to build the neural network model between the largest and other singular values which are coefficient. Secondly, the watermark information has been embedded in selecting larger contrasting image blocks. Finally, the neural networks were used for memorizing the original image and watermark information. While checking, the watermark was extracted by use of embedding watermarking image and associating original image and then the blind detection was realized. The results show that the watermark can be well extracted through the common image processing and compressing operations and the watermarking algorithm is with the good robustness in the geometric distortion.

A novel interferometric synthetic aperture radar (InSAR) signal processing method based on compressed sensing (CS) theory is investigated in this paper. InSAR image formation provides the scene reflectivity estimation along azimuth and range coordinates with the height information. While surveying the height information of the illuminated scene, the data volume enlarges. CS theory allows sparse sampling during the data acquisition, which can reduce the data volume and release the pressure on the record devices. InSAR system which configures two antennas to cancel the common backscatter random phase in each resolution element implies the sparse nature of the complex-valued InSAR image. The complex-valued image after conjugate multiplication that only a phase term proportional to the differential path delay is left becomes sparse in the transform domain. Sparse sampling such as M-sequence can be implemented during the data acquisition. CS theory can be introduced to the processing due to the sparsity and a link between raw data and interferometric complex-valued image can be built. By solving the CS inverse problem, the magnitude image and interferometric phase are generated at the same time. Results on both the simulated data and real data are presented. In comparison with the conventional SAR interferometry processing results, CS-based method shows the ability to keep the imaging quality with less data acquisition.

Most recently, an emerging Compressed Sensing (CS) theory has brought a major breakthrough for data acquisition and recovery. It asserts that a signal, which is highly compressible in a known basis, can be reconstructed with high probability through sampling frequency which is well below Nyquist Sampling Frequency. When applying CS to Remote Sensing (RS) Video imaging, it can directly and efficiently acquire compressed image data by randomly projecting original data to obtain linear and non-adaptive measurements. In this paper, with the help of distributed video coding scheme which is a low-complexity technique for resource limited sensors, the frames of a RS video sequence are divided into Key frames (K frames) and Non-Key frames (CS frames). In other words, the input video sequence consists of many groups of pictures (GOPs) and each GOP consists of one K frame followed by several CS frames. Both of them are measured based on block, but at different sampling rates. In this way, the major encoding computation burden will be shifted to the decoder. At the decoder, the Side Information (SI) is generated for the CS frames using traditional Motion-Compensated Interpolation (MCI) technique according to the reconstructed key frames. The over-complete dictionary is trained by dictionary learning methods based on SI. These learning methods include ICA-like, PCA, K-SVD, MOD, etc. Using these dictionaries, the CS frames could be reconstructed according to sparse-land model. In the numerical experiments, the reconstruction performance of ICA algorithm, which is often evaluated by Peak Signal-to-Noise Ratio (PSNR), has been made compared with other online sparse representation algorithms. The simulation results show its advantages in reducing reconstruction time and robustness in reconstruction performance when applying ICA algorithm to remote sensing video reconstruction.

Compressive Sensing (CS) is a new sampling framework that provides an alternative to the well-known Shannon sampling theory. The basic idea of CS theory is that a signal or image, unknown but supposed to be sparse or compressible in some basis, can be subjected to fewer measurements than the nominal number of pixels, and yet be accurately reconstructed. By designing optical sensors to measure inner products between the scene and a set of test functions according to CS theory, we can use sophisticated computational methods to infer critical scene structure and content for significantly economizing the resources in data acquisition store and transmit. In this paper, we investigate how CS can provide new insights into optical imaging including optical devices. We first give a brief overview of the CS theory and reviews associated fast numerical reconstruction algorithms. Next, this paper explores the potential of several different physically realizable optical systems based on CS principles. In the end, we briefly discuss possible implication in the areas of data compression and optical imaging.

Compressive sensing (CS) is a signal processing technique that enables a signal that has a sparse representation in a known basis to be reconstructed using measurements obtained below the Nyquist rate. Single detector image reconstruction applications using CS have been shown to give promising results. In this study, we investigate the application of CS theory to single detector infrared (IR) rosette scanning systems which suffer from low performance compared to costly focal plane array (FPA) detectors. The single detector pseudoimaging rosette scanning system scans the scene with a specific pattern and performs processing to estimate the target location without forming an image. In this context, this generation of scanning systems may be improved by utilizing the samples obtained by the rosette scanning pattern in conjunction with the CS framework. For this purpose, we consider surface-to-air engagement scenarios using IR images containing aerial targets and flares. The IR images have been reconstructed from samples obtained with the rosette scanning pattern and other baseline sampling strategies. It has been shown that the proposed scheme exhibits good reconstruction performance and a large size FPA imaging performance can be achieved using a single IR detector with a rosette scanning pattern.

Huge expansion of multimedia data in internet has given rise to various unauthorized use and modification. This authenticity problem is equally a constraint in the defense information transmission along with secured transmission. Digital watermarking shows a valid solution to the above problem, since it makes possible authentication and secure transmission of confidential information or any sort of control signals. We introduce a new highly secured Digital Watermarking Algorithm especially for Binary Watermark using Lifting Wavelet Transform (LWT) and Singular Value Decomposition (SVD). Our main motive is to keep watermark intact to the original image and also to increase the data embedding & extraction rate. We use only the LL sub-band (containing an approximation of the image) of the cover image obtained from Lifting Wavelet Transform and then apply Singular Value Decomposition to embed the binary watermark. Moreover due to usage of lifting based wavelet transform and SVD the hardware implementation is better.

Due to the problem that the many digital images can be modified more and more easily, watermarking algorithms are proposed. There are many types of digital watermarking algorithms, such as robust digital watermarking algorithms, watermarking schemes for authentication, and reversible digital watermarking algorithms. However, there are some special images, such as the digital medical images for remote treatment, the military images for tracking object, and the remote sensing images, and they are so sensitive to any modification that we should authenticate the copyright by special technique. And this technique is zero-bit watermarking algorithms. In this paper, about zero-bit watermarking algorithms are surveyed. The basic procedures of these classical algorithms are analyzed, and some suggestion about the zero-bit algorithms is given.