An Efficient Plaintext-Related Chaotic Image Encryption Scheme Based on Compressive Sensing

This paper proposes a digital image compression-encryption scheme based on the theory of compressive sensing and cyclic shift, which use random Gauss matrix and sparse transform to compress the digital image, and then cyclic shift and diffusion operation are developed subsequently to the compressive sensing (CS) phase. The algorithm has three advantages: First, the measurement matrix used in the algorithm is generated by Chebyshev mapping, which increases the key space and reduces the burden of key transmission. Second, The Sigmoid function is used to transform the range of compressed data to 0~255, which are stored as 8-bit binary data, thus further reducing the amount of data transmission and avoiding the expansion of encrypted data. Third, the generation of key stream in encryption phase is related to ciphertext, there are different key streams for different plain images. Thus, our algorithm can resist against the chosen-plaintext and known-plaintext attacks effectively. In addition, the implementation of cyclic shift and diffusion operation further enhances the security of the system. Each pixel of the encrypted image is output in the form of 8-bit integer to facilitate data storage, display and transmission. The experimental results and security analysis show that the algorithm has the advantages of large key space, no obvious statistical characteristics of ciphertext, sensitive to plaintext and key, and able to resist chosen-plaintext attack.

Random measurement matrices play a critical role in successful recovery with the compressive sensing (CS) framework. However, due to its randomly generated elements, these matrices require massive amounts of storage space to implement a random matrix in CS applications. To effectively reduce the storage space of the random measurement matrix for CS, we propose a random sampling approach for the CS framework based on the semi-tensor product (STP). The proposed approach generates a random measurement matrix, where the dimensions of the random measurement matrix are reduced to a quarter (or 1/16, 1/64, and even 1/256) of the number of dimensions, which are used for conventional CS. We then estimate the values of the sparse vector with a modified iteratively re-weighted least-squares (IRLS) algorithm. The results of numerical simulations showed that the proposed approach can reduce the storage space of a random matrix to at least a quarter while maintaining quality of reconstruction. All results confirmed that the proposed approach significantly influences the physical implementation of the CS in images, especially on embedded system and field programmable gate array (FPGA), where storage is limited.

This paper describes a new method for lossy audio signal compression via compressive sensing (CS). In this method, a structured shrinkage operator is employed to decompose the audio signal into three layers, with two sparse layers, tonal and transient, and additive noise, and then, both the tonal and transient layers are compressed using CS. Since the shrinkage operator is able to take into account the structure information of the coefficients in the transform domain, it is able to achieve a better sparse approximation of the audio signal than traditional methods do. In addition, we propose a sparsity allocation algorithm, which adjusts the sparsity between the two layers, thus improving the performance of CS. Experimental results demonstrated that the new method provided a better compression performance than conventional methods did.

Deformation inhomogeneity due to sample–anvil friction in cylindrical compression test

In deep exploration, we often use high-noise and high-resolution astronomical images because of long distance transmission, cosmic noise interference, or other disturbances. Designing an effective method for high-noise astronomical image denoising plays a crucial role in this field. The famous compressed sensing (CS) has shown to be a successful technique for high-resolution image reconstruction. CS is employed to solve the denoising problem in high-noise astronomical images, and a CS reconstruction model and a CS curvelet soft thresholding (NCCST) reconstruction algorithm are proposed simultaneously. In image denoising, the <italic>l</italic>₁-norm reconstruction model based on CS cannot fully consider the internal structure of the image, thus affecting the reconstruction quality. Combined with the <italic>l</italic>₁-norm reconstruction model and group sparse total variation method, the CS reconstruction model is first established. In this framework, the NCCST algorithm uses an adaptive curvelet adjustment operator proposed to select the sparse coefficients in the stage of image sparsity transform, and then a descending VisuShrink threshold is adopted to pick out these reconstructed astronomical image coefficients in each iteration. The experimental results demonstrate that the algorithm proposed can reconstruct clear images from high-noise and high-resolution astronomical images. Meanwhile, it can preserve more astronomical image details, edges, and textures. Even at a low-compression sampling ratio, this algorithm can achieve superior reconstruction performance. The proposed algorithm can be applied in the satellite receiving station to quickly and accurately reconstruct high-resolution astronomical images with high quality.

Image encryption based on compressive sensing and chaos systems

In this paper we face the following problem: how to provide each peer local access to the full information (not just a summary) that is distributed over all \emph{edges} of an overlay network? How can this be done if local access is performed at a given rate? We focus on \emph{large and sparse} information and we propose to exploit the compressive sensing (CS) theory to efficiently collect and pro-actively disseminate this information across a large overlay network. We devise an approach based on random walks (RW) to spread CS random combinations to participants in a random peer-to-peer (P2P) overlay network. CS allows the peer to compress the RW payload in a distributed fashion: given a constraint on the RW size, e.g., the maximum UDP packet payload size, this amounts to being able to distribute larger information and to guarantee that a large fraction of the global information is obtained by each peer. We analyze the performance of the proposed method by means of a simple (yet accurate) analytical model describing the structure of the so called CS sensing matrix in presence of peer dynamics and communication link failures. We validate our model predictions against a simulator of the system at the peer and network level on different models of random overlay networks. The model we developed can be exploited to select the parameters of the RW and the criteria to build the sensing matrix in order to achieve successful information recovery. Finally, a prototype has been developed and deployed over the PlanetLab network to prove the feasibility of the proposed approach in a realistic environment. Our analysis reveals that the method we propose is feasible, accurate and robust to peer and information dynamics. We also argue that centralized and other distributed approaches, i.e., flooding and gossiping, are unfit in the context we consider.

The performances of compression and stapled devices were compared previously in porcine colorectal anastomosis. The compression anastomosis was associated with elevated bursting strength and anastomotic patency in this model as compared with the stapled anastomosis. The purpose of this work was to compare the histopathologic features between compression and stapled methods in the healing of colorectal anastomoses using a porcine model. This was a blinded comparison study. The study was conducted at a single university surgery department. Fifty crossbred pigs were used in this study. Fifty crossbred pigs underwent rectal transection 20 cm from the anal verge and end-to-end compression or stapled anastomosis. The anastomotic tissues were harvested 3, 7, 30, and 90 days postoperatively (n = 5-6). Tissue repair parameters associated with the wound healing were analyzed using image analysis morphometry and histological architecture assessments. A different microscopic pattern of the anastomotic area was shown between groups. Foreign body response was rated (p < 0.001) as minimal in the compression and moderate in the stapled group. The scarring area in the compression anastomosis group, on postoperative day 90 (4 ± 3 × 10(5) μm) was lower (p = 0.016) than in the stapled group (2 ± 1 × 10(6) μm). In addition, the anastomotic line was narrower (p = 0.003) 90 days after surgery in the compression samples (0.77 ± 0.20 mm) compared with that in the stapled group (1.86 ± 0.19 mm). Lastly, in terms of inflammatory cells, the compression biopsies showed lower (p < 0.001) numbers of mononuclear cells, polymorphonuclear cells, and lymphocytes in the anastomotic tissues 30 and 90 days from surgery. The long-term effect of the compression technique on the anastomotic patency in colorectal anastomoses should be further investigated in human studies. Compression anastomotic healing was associated with less foreign body reactions, scarring, and inflammation as compared with stapled anastomoses in a large animal model.

Single image super-resolution based on compressive sensing and improved TV minimization sparse recovery

Explicit entropy error bound for compressive DOA estimation in sensor array

With the explosive growth of photo uploading on the web, traditional photo album compression using individual image coding is needed to be improved to save the storage spaces. Recently, an advance technique of photo album compression via video compression is proposed which utilizes the similarity between photos to improve the compression performance. In this paper, we modify the original scheme to improve the compression performance when photos containing human beings. Experiment results show that the proposed method outperforms the state-of-the-art method by at most 12.7% of bit-rate savings for compressing photo albums with humans. Comparing with traditional JPEG compression, the proposed method achieves 70% to 85% of bit-rate savings.

Matched field processing is a generalized beamforming method that matches received array data to a dictionary of replica vectors in order to locate one or more sources. Its solution set is sparse since there are considerably fewer sources than replicas. Using compressive sensing (CS) implemented using basis pursuit, the matched field problem is reformulated as an underdetermined, convex optimization problem. CS estimates the unknown source amplitudes using the replica dictionary to best explain the data, subject to a row-sparsity constraint. This constraint selects the best matching replicas within the dictionary when using multiple observations and/or frequencies. For a single source, theory and simulations show that the performance of CS and the Bartlett processor are equivalent for any number of snapshots. Contrary to most adaptive processors, CS also can accommodate coherent sources. For a single and multiple incoherent sources, simulations indicate that CS offers modest localization performance improvement over the adaptive white noise constraint processor. SWellEx-96 experiment data results show comparable performance for both processors when localizing a weaker source in the presence of a stronger source. Moreover, CS often displays less ambiguity, demonstrating it is robust to data-replica mismatch.

&lt;p&gt;The main drawback of current ECG systems is the location-specific nature of the systems due to the use of&lt;/p&gt; &lt;p&gt;fixed/wired applications. That is why there is a critical need to improve the current ECG systems to achieve extended patient's mobility and to cover security handling. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Sensing Matrix Selection (SMS) approach are used to provide a robust ultra-low-power approach for normal and abnormal ECG signals. Our simulation results based on two proposed algorithms illustrate 15% increase in Signal to Noise Ratio (SNR) and a good level of quality for the degree of incoherence between the random measurement and sparsity matrices. The simulation results also confirm that the Binary Toeplitz Matrix (BTM) provides the best SNR and compression performance with the highest energy efficiency for random sensing matrix.&lt;/p&gt;

Image compression is a well-established and extensively researched field. The huge interest in it has been aroused by the rapid enhancements introduced in imaging techniques and the various applications that use high-resolution images (e.g. medical, astronomical, Internet applications). The image compression algorithms should not only give state-of-art performance, they should also provide other features and functionalities such as progressive transmission. Often, a rough approximation (thumbnail) of an image is sufficient for the user to decide whether to continue the image transmission or to abort; which accordingly helps to reduce time and bandwidth. That in turn necessitated the development of multi-resolution image compression schemes. The existed multi-resolution schemes (e.g., Multi-Level Progressive method) have shown high computational efficiency, but with a lack of the compression performance, in general. In this thesis, a LOw Complexity Multi-resolution Image Compression (LOCMIC) based on the Hierarchical INTerpolation (HINT) framework is presented. Moreover, a novel integration of the Just Noticeable Distortion (JND) for perceptual coding with the HINT framework to achieve a visual-lossless multi-resolution scheme has been proposed. In addition, various prediction formulas, a context-based prediction correction model and a multi-level Golomb parameter adaption approach have been investigated. The proposed LOCMIC (the lossless and the visual lossless) has contributed to the compression performance. The lossless LOCMIC has achieved a 3% reduced bit rate over LOCO-I, about 1% over JPEG2000, 3% over SPIHT, and 2% over CALIC. The Perceptual LOCMIC has been better in terms of bit rate than near-lossless JPEG-LS (at NEAR=2) with about 4.7%. Moreover, the decorrelation efficiency of the LOCMIC in terms of entropy has shown an advance of 2.8%, 4.5% than the MED and the conventional HINT respectively.

Compressive Sensing (CS) is a stable and robust technique that allows for the sub-sampling of data at a given data rate: ‘compressive sampling’ or ‘compressive sensing’ at rates smaller than the Nyquist sampling rate. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. In this paper, we investigates how CS can provide new insights into coexisting heterogeneous IoT environments. First, we briefly introduce the CS theory with respect to the sampling through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the hub nodes measure, transmit, and store the sampled data into the fusion center. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Therefore, compression should be performed locally at each Access Point (AP) and reconstruction is executed jointly to consider dependencies in the acquired data by the final fusion center.