Quantum realization of the nearest neighbor value interpolation method for INEQR

Zhou

et al. 2019

Sci Rep

Self Cite

A novel and traceable quantum steganography scheme based on pixel value differencing (PVD) is proposed. In the proposed scheme, a quantum cover image is divided into non-overlapping blocks of two consecutive pixels. Then, by a series of reversible logic circuits, we calculate the difference value based on the values of the two pixels in each block and classify it as one of a set of continuous ranges. The secret image and operator information are embedded in the cover image by using the new obtained difference value to replace the original one. The number of bits of secret image that can be embedded in a block is determined, and the number of bits of operator information is decided by the range of the difference value belongs to. Moreover, when the embedded data is extracted from a stego image, it is not necessary to refer to the original cover image. The performance of the proposed scheme is based on the analysis of several categories of simulation results, such as visual quality, capacity, and robustness.

“…Zhou et al . 15 designed a reversible parallel subtractor (RPS) through a series of basic modules. The simplified circuit module of RPS is illustrated in Fig.…”

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Traceable Quantum Steganography Scheme Based on Pixel Value Differencing

Zhou

et al. 2019

Sci Rep

Self Cite

“…Various quantum image representation models have been proposed, including qubit lattice, [11] entangled images, [12] real ket, [13] flexible representation of quantum images, [14] new quantum-enhanced representation (NEQR), [15] quantum image representation of logarithmic polar images, [16] and an improved novel quantum color image representation model (INCQI). [17] Based on these representation models, numerous quantum image processing algorithms have emerged, covering areas such as quantum grayscale and color image scaling, [18][19][20][21] quantum image feature extraction, [22,23] quantum image transformation, [24][25][26] quantum image matching, [27][28][29] quantum image compression, [30] quantum image segmentation, [30,31] quantum image watermarking, [32][33][34][35][36][37] and quantum image encryption. [38][39][40] In image processing, aliasing is a common phenomenon occurring when an image is sampled or scaled, resulting in high-frequency information being erroneously represented as low-frequency information, leading to unnatural artifacts or distortions.…”

Section: Introductionmentioning

confidence: 99%

An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

Wu 吴,

Zhou 周,

Luo 罗

2024

Chinese Phys. B

As a part of quantum image processing, quantum image filtering is a crucial technology in the development of quantum computing. Low-pass filtering can effectively achieve anti-aliasing effects on images. Currently, most quantum image filtering is based on classical domains and grayscale images, with relatively fewer studies on anti-aliasing in the quantum domain. This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain. It achieves the effect of antialiasing filtering on quantum images during the scaling process. First, we use the novel Enhanced Quantum Representation (NEQR) and the Improved Quantum Representation of Color Images (INCQI) to represent classical images. Since aliasing phenomena are more pronounced when images are scaled down, this paper focuses only on the anti-aliasing effects in the case of reduction. Subsequently, we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image, achieving the anti-aliasing effect. The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size. Finally, the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling, applying anti-aliasing filtering to the images results in smoother and clearer outputs. Additionally, it allows for manual intervention to select the desired level of image smoothness.

“…The usual tasks of image processing are performed utilizing the theory of quantum mechanics. This includes image representation (Yan et al 2016), image matching (Jiang et al 2016), similarity analysis (Zhou et al 2018b), interpolation (Zhou et al 2018a), denoising (Mastriani 2015a), coding (Chapeau-Blondeau and Belin 2016), watermarking (Li et al 2016), and segmentation (Caraiman and Manta 2015). These attempts are based on representing the pixels of an image as qubits operated on via suitable quantum circuits.…”

Section: Introductionmentioning

confidence: 99%

A continuous-variable quantum-inspired algorithm for classical image segmentation

Youssry

El-Rafei

Zhou

2019

Quantum Mach. Intell.

The probabilistic nature of quantum particles, state space, and the superposition principle are among the important concepts in quantum mechanics. A framework was previously developed by the authors that allowed to take advantage of these quantum aspects in the field of image processing. This was done by modeling each image's pixel by a two-state quantum system which allowed efficient single-object segmentation. However, the extension of the framework to multiobject segmentation would be highly complex and computationally expensive. In this paper, we propose a classical image segmentation algorithm inspired by the continuous-variable quantum theory that overcomes the challenges in extending the framework to multi-object segmentation. By associating each pixel with a quantum harmonic oscillator, the space of coherent states becomes continuous. Thus, each pixel can evolve from an initial state to any of the continuous coherent states under the influence of an external resonant force. The Hamiltonian operator is designed to account for this force and is derived from the features extracted at the pixel. Therefore, the system evolves from an initial ground state to a final coherent state depending on the image features. Finally by calculating the fidelity between the final state and a set of reference states representing the objects in the image, the state with the highest fidelity is selected. The collective states of all pixels produce the final segmentation. The proposed method is tested on a database of synthetic and natural images, and compared with other methods. Average sensitivity and specificity of 97.86% and 99.61% were obtained respectively indicating the high segmentation accuracy of the algorithm.