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In a wireless sensor network, the sensor nodes transmit the acquired information to the server through the data transmission link. On the serverside, the data are processed, fused, and expressed to serve the user. Sensor deployment is a key factor related to the stability and security of wireless networks. This article uses environmental changes to drive related technologies to deploy wireless sensors. In this article, environmental change-driven means that through certain deployment cost model assumptions and problem descriptions, network deployment is artificially divided into two stages: initial deployment and redeployment. In the deployment phase, by referring to the idea of virtual force, a new sensor deployment algorithm is proposed in the redeployment phase, which can well solve the stability- and security-related issues encountered in agricultural wireless sensor networks. In this algorithm, the moving distance of the mobile receiver and the average coverage of the network are calculated based on the virtual force, the direction, and the number of adjacent clusters. Finally, the algorithm model was simulated in MATLAB, and the feasibility of the algorithm was verified by analyzing the event coverage and the moving distance of nodes. The final simulation results show that the algorithm proposed in this paper can achieve better performance than existing algorithms in terms of average coverage and moving distance.

In some image processing algorithms, such as those for image feature extraction and segmentation, filtering is a significant pre-processing step to remove noises and improve image quality. An improved quantum image median filtering approach is proposed, and its corresponding quantum circuit is designed in this work. The main idea of the approach is that first the classical image is converted into a quantum version based on the novel enhanced quantum representation (NEQR) of digital images, and then a unique quantum module is designed to realize the median calculation of neighborhood pixels for each pixel point in the image. Finally, in order to improve the filtering effect, extremum detection is employed to distinguish noises from true signals. The experimental results show that a competitive filtering performance is obtained compared with previous methods. In addition, a network complexity analysis of the quantum circuit suggests that the proposed filtering approach can perform enormous speed-up over its corresponding classical counterparts.

This paper investigates a quantum watermarking scheme using image interpolation method. Based on the novel enhanced quantum representation of digital images (NEQR), a new scaling-up quantum carrier is generated via Neighbor Mean Interpolation (NMI) method first. Then, three binary watermark images are formed a conceptual quantum watermark image with 3-qubit grayscale based on NEQR, and it is embedded into the new generated quatnum carrier image pixel’s least significant bit (LSB). To achieve this aim, the Quantum Fourier Transform (QFT) is introduced first. Based on QFT operations, several QFT based quantum modules, i.e., quantum Adder, Subtractor and Divider are defined as well as some others commonly used quantum modules also reviewed. Following that, based on these quantum modules, the quantum circuit for image scaling-up is presented first, which can generate a new enlarged quantum carrier image. Then, based on the generated quantum carrier image, we further illustrate the integrated quantum circuit for how to embed quantum watermark images into quantum grayscale carrier images. Finally, the quantum circuit complexity is analyzed, which proves that we presented quantum watermarking scheme can be realized within polynomial time complexity, and the experiment results based on classical computer (i.e., non-quantum version) are simulated under the classical computer software MATLAB 2016a, which demonstrates that we investigated method has an acceptable visual effect and also easy to detect the attacks.

The discrete cosine transform (DCT) is widely used in image and video compression standard formats. This is due to its ability to represent signals and images using a limited number of significant coefficients without noticeable loss of visual clarity. The classical one-dimensional discrete cosine transform (1D - DCT) and two-dimensional discrete cosine transform (2D - DCT) have computational complexities of O(Nlog2N) and O(N2log2N), respectively. Thus, as the images grow in size, the runtime of the DCT highly increases which could limit its usability in real-time applications. This paper presents a quantum DCT algorithm (QDCT) that is more efficient than its classical counterpart in terms of complexity. Furthermore, the proposed QDCT is used to develop and realize a quantum image compression technique. The developed compression technique performs a search to determine the most significant computed DCT coefficients and is derived from Grover’s algorithm. It provides a generalization to the original search algorithm by utilizing two oracle operators to solve the complex unstructured search problem rather than a single one. Thus, the proposed QDCT algorithm can simultaneously calculate the DCT coefficients and identify the significant DCT coefficients through applying two oracles. The comparison of the introduced QDCT with Grover’s algorithm also indicates that the QDCT algorithm is more efficient. This can be attributed to performing a rotation on the subspace rather than on the global space in Grover’s algorithm. In addition, the presented quantum 1D - and 2D - DCT have reduced complexities compared to the classical algorithms which are O ( N ) and O(N), respectively. Therefore, the presented QDCT and compression algorithm can be applied efficiently to accomplish various transform-based quantum signal and image processing tasks.

In this paper, a novel frequency domain quantum watermarking scheme is proposed based on the Flexible Representation of Quantum Images, which can embed a 2n1 × 2n1 binary watermark image into a 2n × 2n grayscale carrier image. The quantum Haar wavelet transform is developed and used to decompose quantum images. The diagonal detail coefficients of the carrier image are obtained from the image decomposition. Then, according to the watermark image information, the diagonal wavelet coefficients are either unchanged or slightly modified. Since all of the used quantum operations are invertible, extraction of the watermark image is performed in a straightforward manner by reversing the watermarking embedding process. Finally, the proposed quantum image watermarking scheme is simulated on a classical computer and evaluated under different carrier and watermark images. The simulation results and performance analyses indicate the high performance of the presented watermarking scheme in terms of the similarity between the watermarked and carrier images.

A novel quantum steganography scheme is investigated based on efficient embedding algorithm of modified exploiting modification direction in this paper, which embeds two $$ 2^{n} \times 2^{n} $$ binary images (secret information) into a $$ 2^{n} \times 2^{n} $$ color image (carrier image). To improve the security of the secret information and enhance the robustness of the proposed scheme, a $$ 2^{n} \times 2^{n} $$ binary image (secret key) is generated according to the exclusive or operation of two secret images. The secret key acts as the control key of choosing two channels from the carrier image’s three channels of R, G and B (i.e., R, G or R, B channels chose as the pixel-group) to hide the secret information. In addition, the effective quantum circuits for investigated quantum steganography scheme are illustrated to better understanding the procedure of embedding algorithm. The experiment results simulated on the classical computer with MATLAB environment, and the numerical analyses demonstrate that the presented algorithm has good performance on imperceptibility and security.

We propose a new quantum watermarking scheme based on threshold selection using informational entropy of quantum image. The core idea of this scheme is to embed information into object and background of cover image in different ways. First, a threshold method adopting the quantum informational entropy is employed to determine a threshold value. The threshold value can then be further used for segmenting the cover image to a binary image, which is an authentication key for embedding and extraction information. By a careful analysis of the quantum circuits of the scheme, that is, translating into the basic gate sequences which show the low complexity of the scheme. One of the simulation-based experimental results is entropy difference which measures the similarity of two images by calculating the difference in quantum image informational entropy between watermarked image and cover image. Furthermore, the analyses of peak signal-to-noise ratio, histogram and capacity of the scheme are also provided.

Quantum image processing has recently emerged as an essential problem in practical tasks, e.g. real-time image matching. Previous studies have shown that the superposition and entanglement of quantum can greatly improve the efficiency of complex image processing. In this paper, a fuzzy quantum image matching scheme based on gray-scale difference is proposed to find out the target region in a reference image, which is very similar to the template image. Firstly, we employ the proposed enhanced quantum representation (NEQR) to store digital images. Then some certain quantum operations are used to evaluate the gray-scale difference between two quantum images by thresholding. If all of the obtained gray-scale differences are not greater than the threshold value, it indicates a successful fuzzy matching of quantum images. Theoretical analysis and experiments show that the proposed scheme performs fuzzy matching at a low cost and also enables exponentially significant speedup via quantum parallel computation.

In recent years, quantum image processing is one of the most active fields in quantum computation and quantum information. Image scaling as a kind of image geometric transformation has been widely studied and applied in the classical image processing, however, the quantum version of which does not exist. This paper is concerned with the feasibility of the classical bilinear interpolation based on novel enhanced quantum image representation (NEQR). Firstly, the feasibility of the bilinear interpolation for NEQR is proven. Then the concrete quantum circuits of the bilinear interpolation including scaling up and scaling down for NEQR are given by using the multiply Control-Not operation, special adding one operation, the reverse parallel adder, parallel subtractor, multiplier and division operations. Finally, the complexity analysis of the quantum network circuit based on the basic quantum gates is deduced. Simulation result shows that the scaled-up image using bilinear interpolation is clearer and less distorted than nearest interpolation.

Based on the modified flexible representation of quantum images, a novel quantum image encryption algorithm was proposed in this paper. The encryption process performs Arnold scrambling operation to disturb the quantum image information in spatial domain first. Then, quantum wavelet transforms are employed to decompose the scrambled quantum image into multiscale resolution (i.e., a sequence of subimages) in the frequency domain, which are mainly divided into two parts: the low-frequency component (i.e., the approximation) and high-frequency detail information (i.e., the horizontal details, vertical details and diagonal details in each decomposition level). Following that, Arnold scrambling operations are implemented to encrypt the wavelet coefficients within each subimage in the frequency domain once again. Finally, based on inverse quantum wavelet transforms, the encrypted wavelet coefficients can affect the pixel values of the entire reconstructed quantum images. Due to the fact that all the quantum operations are invertible, the decryption process of the encrypted image is performed in a straightforward manner by reversing all of the quantum operations within quantum image encryption process. The proposed encryption algorithm is simulated on a classical computer with MATLAB environments. Experimental results and numerical analysis indicate that the presented algorithm has a good encrypted effect and high security.

With the increasing of depth and complexity of the convolutional neural network, parameter dimensionality and volume of computing have greatly restricted its applications. Based on the SqueezeNet network structure, this study introduces a block convolution and uses channel shuffle between blocks to alleviate the information jam. The method is aimed at reducing the dimensionality of parameters of in an original network structure and improving the efficiency of network operation. The verification performance of the ORL dataset shows that the classification accuracy and convergence efficiency are not reduced or even slightly improved when the network parameters are reduced, which supports the validity of block convolution in structure lightweight. Moreover, using a classic CIFAR-10 dataset, this network decreases parameter dimensionality while accelerating computational processing, with excellent convergence stability and efficiency when the network accuracy is only reduced by 1.3%.

Capacity and invisibility are two targets of the methods for quantum image steganography. To hide large messages into the quantum cover image and remain invisible is still a challenge. In this paper, we propose an efficient quantum image steganography scheme, which can be finalized by applying an optimal pixel adjustment process to the quantum secret image (constructed by k binary images) before it is embedded. In particular, each pixel of quantum secret image will be inverted or not inverted before it is embedded. The decisions are recorded by a quantum key image for extracting data. Then, it is accomplished by embedding the quantum secret image into the k least significant bits of the quantum cover image using the basic quantum gates and composite quantum modules. Because all quantum operations are invertible, the extraction procedure is the inverse of the embedding procedure. Obviously, since the value of k can be changed, the proposed quantum steganography scheme can provide variable embedding capacity. Numerical simulation and theoretical analysis have shown that our method has outperformed other similar schemes in terms of the visual quality and embedding capacity. In addition, it also provides a lower computational complexity than its classical counterpart and other quantum steganography schemes.

As an important way to protect copyright by embedding watermark in digital images, quantum watermarking catches more and more attentions. In this study, a novel quantum watermarking method on the basis of tri-way pixel value differencing and modified least significant bit (LSB) substitution is proposed. A quantum cover image using the novel-enhanced quantum image representation is partitioned into non-overlapping 2 × 2 blocks with four pixels firstly. To classify the block as a smooth area or an edge area, the tri-way pixel value differences are calculated and compared with a predefined threshold. The quantum watermark image, which is expanded and scrambled, is then embedded into a quantum cover image by the k-bit LSB substitution method, where k is decided by the level of each block. The embedded quantum watermark can be extracted from the quantum stego-image without the assistance of original quantum cover image. Theoretical analysis and simulation-based experiments demonstrate both the feasibility and capabilities of the proposed quantum watermarking method, which has good visual quality, better robustness, and higher security.

In order to solve the high complexity of classical image processing, a quantum scheme for image sharpness estimation based on the Laplacian operator is proposed. The mean of grayscale gradients of all pixels is regarded as the sharpness estimation metric. A new quantum image representation model is presented by extending the Novel Enhanced Quantum Representation (NEQR) model, which is greatly useful for quantum image convolution. In quantum platforms, it has been shown that the mean calculation of numbers is rather difficult because the numbers are stored in a quantum superposition state. In order to solve this problem, we put forward an algorithm which essential idea is cyclically shifting the superposition state and iteratively calculating the mean of the new one and the original state. The mean can be obtained from the superposition state by only one quantum measurement. By analyzing the space complexity and time complexity, the scheme is far superior to classical ones in terms of resource consumption and execution speed. In addition, the results of simulation experiments show that for noiseless images, the performance of the scheme is consistent with subjective visual perception of images sharpness.

Edge detection, as a fundamental problem in image processing and computer vision, is an indispensable task in digital image processing. Because of the sharp increase in the image data in the actual applications, real-time problem has become a limitation in classical image processing. In this paper, based on the novel enhanced quantum image representation (NEQR) of digital images, an enhanced quantum edge detection algorithm is investigated, which combines the classical Laplacian operator and zero-cross method. Because NEQR utilizes the superposition state of qubit sequence to store all the pixels of an image, the corresponding quantum image edge detection algorithm can realize parallel computation to implement the Laplacian filter and further calculate the image intensity of all the pixels according zero-cross method. The circuit complexity analysis demonstrates that our presented quantum image edge algorithm can reach a significant and exponential speedup compared to classical counterparts. Hence, our proposed quantum image edge detection algorithm would resolve the real-time problem of image edge extraction in practice image processing.

As the basic problem in image processing and computer vision, the purpose of edge detection is to identify the point where the brightness of the digital image changes obviously. It is an indispensable task in digital image processing that image edge detection significantly reduces the amount of data and eliminates information that can be considered irrelevant, preserving the important structural properties of the image. However, because of the sharp increase in the image data in the actual applications, real-time problem has become a limitation in classical image processing. In this paper, quantum image edge extraction for the novel enhanced quantum representation (NEQR) is designed based on classical Sobel operator. The quantum image model of NEQR utilizes the inherent entanglement and superposition properties of quantum mechanics to store all the pixels of an image in a superposition state, which can realize parallel computation for calculating the gradients of the image intensity of all the pixels simultaneously. Through constructing and analyzing the quantum circuit of realization image edge extraction, we demonstrate that our proposed scheme can extract edges in the computational complexity of $$\mathrm{O}({n^2} + {2^{q + 4}})$$ for a NEQR quantum image with a size of $${2^n} \times {2^n}$$. Compared with all the classical edge extraction algorithms and some existing quantum edge extraction algorithms, our proposed scheme can reach a significant and exponential speedup. Hence, our proposed scheme would resolve the real-time problem of image edge extraction in practice image processing.

This paper presents the nearest neighbor value (NNV) interpolation algorithm for the improved novel enhanced quantum representation of digital images (INEQR). It is necessary to use interpolation in image scaling because there is an increase or a decrease in the number of pixels. The difference between the proposed scheme and nearest neighbor interpolation is that the concept applied, to estimate the missing pixel value, is guided by the nearest value rather than the distance. Firstly, a sequence of quantum operations is predefined, such as cyclic shift transformations and the basic arithmetic operations. Then, the feasibility of the nearest neighbor value interpolation method for quantum image of INEQR is proven using the previously designed quantum operations. Furthermore, quantum image scaling algorithm in the form of circuits of the NNV interpolation for INEQR is constructed for the first time. The merit of the proposed INEQR circuit lies in their low complexity, which is achieved by utilizing the unique properties of quantum superposition and entanglement. Finally, simulation-based experimental results involving different classical images and ratios (i.e., conventional or non-quantum) are simulated based on the classical computer’s MATLAB 2014b software, which demonstrates that the proposed interpolation method has higher performances in terms of high resolution compared to the nearest neighbor and bilinear interpolation.

Based on the NEQR of quantum images, a new quantum gray-scale image watermarking scheme is proposed through Arnold scrambling and least significant bit (LSB) steganography. The sizes of the carrier image and the watermark image are assumed to be $$2n\times 2n$$ and $$n\times n$$ , respectively. Firstly, a classical $$n\times n$$ sized watermark image with 8-bit gray scale is expanded to a $$2n\times 2n$$ sized image with 2-bit gray scale. Secondly, through the module of PA-MOD N, the expanded watermark image is scrambled to a meaningless image by the Arnold transform. Then, the expanded scrambled image is embedded into the carrier image by the steganography method of LSB. Finally, the time complexity analysis is given. The simulation experiment results show that our quantum circuit has lower time complexity, and the proposed watermarking scheme is superior to others.

Based on the novel enhanced quantum representation for quantum image (NEQR), a new blind quantum steganography scheme is proposed. In this scheme, an improved quantum representation of text utilizing American Standard Code for Information Interchange (ASCII) is provided that uses two qubit sequences to store the same quantum text message. The general embedding process of the scheme is as follows: First, the cover image of size [Formula: see text] will be divided into eight blocks of size [Formula: see text] and the secret quantum text of size [Formula: see text] is scrambled by Gray code transform method. Then, the disorder quantum text is embedded into the eight blocks of cover image employing the Gray code as a judgment condition. Meanwhile, the corresponding quantum circuits are drawn. Through the analysis of all quantum circuits, it can be concluded that the scheme has a lower complexity that is [Formula: see text]. And the performance of the proposed scheme is analyzed in terms of simulation results of three items: visual quality, capacity and robustness.

The high-precise star sensor calibration method requires high-accurate turntable, collimator, star point plate or other high-precision devices that are very expensive. We present a simple and available method to calibrate the principal point, focal length, radial distortion, tangential distortion and installation error of star sensor in laboratory, and without having high accurate or expensive devices. The calibration model takes the ordinary camera calibration methods and installation error into account. The installation error is modeled by combination of three typical effects: the installation of pan-tilt-zoom (PTZ) initial status, PTZ and charge-coupled device, which result in six parameters. The proposed procedure consists of a closed-form solution, followed by a nonlinear refining based on maximum likelihood criterion. Our calibration method is validated through simulation and real data that shows the superiority with respect to the traditional methods and has the same level as the state-of-the-art methods. The accuracy of our calibration method is 0.015° in the root of mean square distances between testing points and projected ones.

Based on the NEQR representation of quantum grayscale images and binary images, the optimal LSBs-based quantum watermarking scheme is investigated in this paper, which embeds several binary images (watermark images) into a grayscale image (cover image). The size of the cover image and secret image are both assumed to be [Formula: see text]. Compared to quantum simple LSBs substitution method generating one stego-pixel, our presented quantum optimal LSBs-based method can generate three stego-pixel simultaneously first. Then one of them with lowest visual distortion is chosen as the final stego-pixel. To this end, first of all, the quantum circuits for a few basic quantum modules (i.e. Quantum Comparator, Parallel CNOT, Parallel Swap, ADDER MOD, Subtracter (SUB.ER) MOD and Absolute Value) are predefined. Following that, based on these simple modules, two composite quantum modules (i.e. the ADDER and SUB.ER MOD [Formula: see text] module and Choose final stego-pixel module) are further constructed. With the help of the basic and composite quantum modules, the integrated quantum circuit implementation of the optimal LSBs-based quantum watermark images embedding/extracting procedures are illustrated. Then, the experiment result are simulated under the classical computer software MATLAB 2014(b), which illustrates our presented optimal LSBs-based quantum watermarking methods are superior to the simple LSBs scheme in terms of PSNR and histogram graphs on the basis of visual effect, and the circuit’s complexity analysis also demonstrates our investigated scheme with a very low computational complexity. Finally, we analyze the security of quantum cryptography system, which verifies the quantum watermarking data can be securely transmitted to other legal normal users.