3D image encoding in FT domain based on CGII algorithm

Li, Xiao Wei; Kim, Seok Tae; Lee, In-Kwon

doi:10.1016/j.apm.2014.11.063

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…It is very vital for an effective encryption method to be sensitive to the encryption keys, thus the key space of the encryption method should be large enough to resist brute-force attacks [28][29][30][31][32]. Recently, cellular automata (CA) based image encryption methods have been presented because of the capacity of parallel calculation and high security.…”

Section: Introductionmentioning

confidence: 99%

Modified integral imaging reconstruction and encryption using an improved SR reconstruction algorithm

Wang

et al. 2019

Optics and Lasers in Engineering

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We propose a monospectral image encryption method in which the multispectral color image acquisition by using heterogeneous monospectral cameras. Because the captured monospectral elemental images (EIs) belongs to grayscale image, it is means that the captured EIs can be directly encrypted by the proposed encoding method. Subsequently, the linear cellular automata (CA) and hyperchaotic encoding algorithm are employed to encrypt the captured EIs. Different from previous methods, the proposed method can directly encrypt the multispectral color information rather than having to divide into three color channels (R, G and B), thereby, the proposed method can greatly reduce the encryption calculation. MonospectralEIs encrypted by linear CA and hyper-chaotic system 2.2.1. M-sequence generated by linear CA CA can offer significant benefit over existing algorithms [33,34]. In a one-dimensional (1D), two-state, three-site neighbourhood CA, where the next state of a cell is updated according to its neighborhood, the value of each cell is 0 or 1. The value of each cells can be generated by a specified rule. The value of next state is calculated by the Boolean function with three parameters.where s i (t + 1) denotes the value of cell i at time t + 1, s i (t) denotes the value of the cell i at time t, y i−1 (t) is the the left neighboring cell value at time t, s i+1 (t) is the right neighboring cell value at time t, and F () represents the Boolean function defining a specified rule. According his theory of Wolfram, for the 2-state, 3-site CA, it has 2 8 rules. The Wolfram rules are defined from 0 to 255. Among the rules, eight Wolfram rules are linear. They are 0, 60, 90, 102, 150, 170, 204, and 240, respectively. The eight Wolfram linear rules are represented as follows:Rule 0 : s i (t + 1) = 0, Rule 60 : s i (t + 1) = s i−1 (t) ⊕ s i (t), Rule 90 : s i (t + 1) = s i−1 (t) ⊕ s i+1 (t), Rule 102 : s i (t + 1) = s i (t) ⊕ s i+1 (t), Rule 150 : s i (t + 1) = s i−1 (t) ⊕ s i (t) ⊕ s i+1 (t), Rule 170 : s i (t + 1) = s i−1 (t), Rule 204 : s i (t + 1) = s i (t), Rule 240 : s i (t + 1) = s t−1 (t).

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