[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

비만아 부모의 돌봄 경험: q 방법론

Cosine-transform-based chaotic system for image encryption

2D Logistic-Sine-coupling map for image encryption

Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.

A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications

Most image encryption algorithms based on low-dimensional chaos systems bear security risks and suffer encryption data expansion when adopting nonlinear transformation directly. To overcome these weaknesses and reduce the possible transmission burden, an efficient image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing is proposed. The original image is measured by the measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the cycle shift operation controlled by a hyper-chaotic system. Cycle shift operation can change the values of the pixels efficiently. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys distribution simultaneously as a nonlinear encryption system. Simulation results verify the validity and the reliability of the proposed algorithm with acceptable compression and security performance.

Image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing

Image compression and encryption scheme based on 2D compressive sensing and fractional Mellin transform

Double-image encryption scheme combining DWT-based compressive sensing with discrete fractional random transform

Based on hyper-chaotic system and discrete fractional random transform, an image compression-encryption algorithm is designed. The original image is first transformed into a spectrum by the discrete cosine transform and the resulting spectrum is compressed according to the method of spectrum cutting. The random matrix of the discrete fractional random transform is controlled by a chaotic sequence originated from the high dimensional hyper-chaotic system. Then the compressed spectrum is encrypted by the discrete fractional random transform. The order of DFrRT and the parameters of the hyper-chaotic system are the main keys of this image compression and encryption algorithm. The proposed algorithm can compress and encrypt image signal, especially can encrypt multiple images once. To achieve the compression of multiple images, the images are transformed into spectra by the discrete cosine transform, and then the spectra are incised and spliced into a composite spectrum by Zigzag scanning. Simulation results demonstrate that the proposed image compression and encryption algorithm is of high security and good compression performance.

Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform

Disclosed is an image encryption method based on two-dimensional compressed sensing and non-linear fraction Mellin transform Part of Hadamand matrix built through a chaotic system serves as a measurement matrix, projection measurement is carried out on an original image subjected to two-dimensional discrete cosine transform in two orthogonal directions to obtain a measurement value, compression encryption of the image is realized, and the defect of a too large data amount after fraction Mellin transform is carried out on the image is overcome; non-linear fraction Mellin transform is carried out on the measurement value according to an annular range to obtain a ciphertext, and the problem that a linear compressed sensing encryption system is low in safety is resolved The initial value of the chaotic system and the transform order of fraction Mellin transform serve as the main secret key, and the central coordinates of coordinate transform and inner radius and outer radius of the transform implementation area serve as a secondary secret key The two-dimensional compressed sensing technology and non-linear fraction Mellin transform are effectively combined, the consumption amount of the secret keys is small, the secret key space is large, and high safety is realized

Shumin Pan

Papers

Image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing

Image compression and encryption scheme based on 2D compressive sensing and fractional Mellin transform

Double-image encryption scheme combining DWT-based compressive sensing with discrete fractional random transform

Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform

Image encryption method based on two-dimensional compressed sensing and non-linear fraction Mellin transform