The need for information security has become urgent due to the constantly changing nature of the Internet and wireless communications, as well as the daily generation of enormous volumes of multimedia. In this paper, a 3-stage image cryptosystem is developed and proposed. A tan variation of the logistic map is utilized to carry out deoxyribonucleic acid (DNA) encoding in the first stage. For the second encryption stage, the numerical solution of the Lorenz differential equations and a linear descent algorithm are jointly employed to build a robust S-box. The logistic map in its original form is utilized in the third stage. Diffusion is guaranteed through the first and third encryption stages, while confusion is guaranteed through the application of the S-box in the second encryption stage. Carrying out both confusion- and diffusion-inducing stages results in encrypted images that are completely asymmetric to their original (plain) counterparts. An extensive numerical analysis is carried out and discussed, showcasing the robustness and efficacy of the proposed algorithm in terms of resistance to visual, statistical, entropy, differential, known plaint text and brute-force attacks. Average values for the computed metrics are: Information entropy of 7.99, MSE of 9704, PSNR of 8.3 dB, MAE of 80.8, NPCR of 99.6 and UACI of 33. The proposed algorithm is shown to exhibit low computational complexity, encrypting images at an average rate of 1.015 Mbps. Moreover, it possesses a large key space of 2372, and is demonstratd to successfully pass all the tests of the NIST SP 800 suite. In order to demonstrate the superior performance of the proposed algorithm, a comparison with competing image encryption schemes from the literature is also provided.

In this paper, we propose an edge-based junction detector. In addition to detecting the locations of junctions, this operator specifies their orientations as well. In this respect, a junction is defined as a meeting point of two or more ridges in the gradient domain into which an image can be transformed through Gaussian derivative filters. To accelerate the detection process, two binary edge maps are produced; a thick-edge map is obtained by imposing a threshold on the gradient magnitude image, and another thin-edge map is obtained by calculating the local maxima. Circular masks are centered at putative junctions in the thick-edge map, and the so-called circumferential anchors or CA points are detected in the thin map. Radial lines are scanned to determine the presence of junctions. Comparisons are made with other well-known detectors. This paper proposes a new formula for measuring the detection accuracy. In addition, the so-called junction coordinate systems are introduced. Our operator has been successfully used to solve many problems such as wide-baseline matching, 3-D reconstruction, camera parameter enhancing, and indoor and obstacle localization.

This paper presents a new junction detection operator that defines junctions as points where linear ridges in the gradient domain intersect. The radial lines that compose the junction are therefore identified by searching, in a circular neighborhood, for directional maxima of the intensity gradient. The proposed algorithm operates on two binary edge maps, the computational complexity of the detection process is then considerably reduced.

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With advancements in computer and communication technologies, the production, utilization and applications of digital images is at an unprecedented rate. Recent applications include military communications, remote sensing, novel engineering designs storage and communications, as well as medical imaging. In most cases, such images convey highly sensitive or confidential information, which creates a strong need for the design of secure and robust color image cryptosystems. Recent literature has shown that fractional-order functions exhibit improved performance over their corresponding integer-order versions. This is especially true in their use in image processing applications. In this research work, we make use of a four-dimensional (4D) hyperchaotic Chen map of fractional-order, in conjunction with a sine chaotic map and a novel hybrid DNA coding algorithm. A thorough numerical analysis is presented, showcasing the security performance and efficiency of the proposed color image cryptosystem. Performance is gauged in terms of resilience against visual, histogram, statistical, entropy, differential, as well as brute-force attacks. Mean values of the metrics computed are as follows. MSE of 9396, PSNR of 8.27 dB, information entropy of 7.997, adjacent pixel correlation coefficient of 0, NPCR of 99.62%, UACI of 33, MAE of 80.57, and a very large key space of 2 744 . The proposed image cryptosystem exhibits low computational complexity, as it encrypts images at a rate of 4.369 Mbps. Furthermore, it passes the NIST SP 800 suite of tests successfully. Comparison of the computed metrics of the proposed image cryptosystem against those reported in the stateof-the-art by counterpart algorithms show that the proposed cryptosystem exhibits comparable or superior values.