Abstract Linear gray enhancement is a spatial domain image enhancementtechnique commonly used in classical computers, mainly including image negative, image contrast stretching, and piecewise linear gray transformation. Inorder to realize these three linear gray enhancement techniques in the quantumcomputers, this paper proposes three types of linear gray transformationschemes for quantum images based on the generalized model of novel enhancedquantum image representation(GNEQR), and the quantum circuits that realizethese three transformation methods are constructed according to theschemes. The proposed circuits take advantage of efficient quantum arithmeticoperations and parallel Controlled-NOT modules to factor classical transformationsinto basic unitary operators such as the Controlled-NOT gates andthe Toffoli gates. The results show that the linear gray enhancement algorithmfor quantum images is better than the classical algorithm in both spatialcomplexity and time complexity.
Abstract Linear gray enhancement is a spatial domain image enhancementtechnique commonly used in classical computers, mainly including image negative, image contrast stretching, and piecewise linear gray transformation. Inorder to realize these three linear gray enhancement techniques in the quantumcomputers, this paper proposes three types of linear gray transformationschemes for quantum images based on the generalized model of novel enhancedquantum image representation(GNEQR), and the quantum circuits that realizethese three transformation methods are constructed according to theschemes. The proposed circuits take advantage of efficient quantum arithmeticoperations and parallel Controlled-NOT modules to factor classical transformationsinto basic unitary operators such as the Controlled-NOT gates andthe Toffoli gates. The results show that the linear gray enhancement algorithmfor quantum images is better than the classical algorithm in both spatialcomplexity and time complexity.
This article explores the use of quantum computing for image processing and inversion. The advantages of quantum data processing methods over classical methods are analyzed. A quantum circuit can solve the fundamental image processing task of image inversion. The application of quantum NOT gates to alter pixel states corresponding to grayscale image values was investigated. It has been demonstrated that using amplitude angles to encode pixel values enables the efficient creation of quantum superposition on an image. The solution involved generating a negative of the image using the X-gate and the Hadamard operation. A quantum circuit was designed and tested on a simulator; an algorithm was also proposed for converting measurement results into a finalized image. The study highlights the advantages and disadvantages of the quantum approach, the linearity of transformations, and scalability prospects. Quantum image processing algorithms can enhance performance when handling large datasets.
Image scrambling on classical computers has been widely studied, however, the corresponding quantum algorithms are rare. This paper proposes a block-based image scrambling scheme for the generalized model of novel enhanced quantum representation (GNEQR). It is not only suit for grayscale images and color images, but also can be used in 2n× 2mimages (i.e., rectangular images). To realize the image scrambling method, we use geometric transformations and an operation of bitplane scrambling to perform position and pixel scrambling, respectively. Furthermore, the complexity of the implementation circuit of the proposed image scrambling is only O(n2) for a 2n× 2nimage. Therefore, it shows our proposed quantum image scrambling is efficient. Then, the result analysis of simulation experiments demonstrates that our proposed image scrambling is effective. In addition, security analysis shows that the proposed scrambling algorithm is secure.
For some image processing algorithms such as edge detection and segmentation, filtering in the spatial domain is an important pre-processing to improve the quality of the image by removing the noise in the images. The time and memory requirements for processing also increase as the size of the images increases. Besides, excessive blurring of the image during noise removal operation can lead to excessive deterioration of image quality. Therefore, the balance between noise reduction and blur needs to be well adjusted. In this paper, a new quantum method for noise reduction on quantum images using QFT-based arithmetic operators is proposed and quantum circuits are designed. Mean filtering operators of different sizes are used to remove noise. First, the classical image is represented by the gray scale quantum image model, the novel enhanced quantum representation (NEQR) model. A quantum method is proposed and circuits are presented for the realization of Mean filters of different sizes on the basis of addition and division operations. Finally, the performance of the method is evaluated by presenting the circuit complexity of the method and the experimental results. In the proposed method, it is aimed to use less resources and reduce the circuit complexity. The optimal filtering operator size is investigated for the balance between noise reduction and image blur.
According to the principles of quantum physics, the computing power of a quantum machine is immense compared to that of a classical one. Encouraged by this idea important applications of quantum computation to computer science have been developed (e.g. faster algorithms, secure transmissions). In this paper we investigate the implications of using quantum computing systems in the field of image processing. We consider basic gray level transformations such as image negatives, binarization, histogram computation and histogram equalization and show how these operations can be expressed using the quantum formalism. Moreover, we show how the efficient exploitation of the special properties of quantum computation leads to better performance for the proposed quantum operations as compared to the classical correspondents.
Quantum circuits to realize color operations of channel of interest, channel swapping, and alpha blending on images are proposed using five kinds of quantum gates, i.e., NOT, CNOT, Toffoli, Rotation, and Controlled Rotation gates. Complexities of the proposed circuits are for an N-sized image, whereas the color information must be transformed pixel by pixel in the case of operators on classical computers. Simulations on the proposed three quantum color operations using three human facial and one Japanese style house images demonstrate that at most 9, 3, and 5 basic quantum gates are requested, that shows the feasibility of quantum circuits. Based on proposed three operations, all invertible classical color information transformation on imagesmay be designed and many applications can be realized on quantum computer, and the channel of interest based watermarking is being researched which the experiment results show that from the point of PSNR, our proposal is about 10 dB better than the chosen method of quantum image watermarking.
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.