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Quantum image representation is an important research direction of quantum image processing, which mainly studies the use of quantum properties to encode and store images. At present, most quantum image representation models store image grayscale-related information, but lack histogram-related information. A few quantum representation models that store image histogram information cannot accurately retrieve the image, resulting in the difficulty of quantum image histogram processing. Therefore, this paper proposes an improved order-encoded quantum image representation model ONEQR, which uses the quantum basis state sequence to store the grayscale information, real position information and ascending position information of the image, which can not only solve the problem of inability to accurately retrieve the image, but also store the histogram information of the image, which is convenient for the implementation of the histogram processing algorithm of quantum image; In addition, based on ONEQR, a quantum image representation model MULTI-ONEQR that stores multiple images is proposed, and based on this model, a quantum image histogram specification (HS) algorithm is proposed, which can use quantum properties to complete image HS in parallel. Finally, the simulation experiment of the HS algorithm based on the ONEQR model is carried out to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Quantum edge detection is one of the important part of quantum image processing. In this paper, a quantum edge detection algorithm is designed for the quantum representation of multi-wavelength image (QRMW) model. The algorithm includes all stages of filtering, enhancement and detection. The proposed algorithm is also designed to apply any filtering operation to QRMW images, not only for a particular filtering operation. The proposed algorithm aims to solve the problems that quantum edge detection algorithms in the literature have processing only for a particular operator and noise reduction. Moreover, the algorithm aims to perform operations more efficiently by using less resources. Low-pass filter (LPF) smoothing operators are applied in the filtering stage for the noise reduction problem. In order to apply all filtering operations to the image, arithmetic operators that can operate with all signed integers are used in the algorithm. The operators Sobel, Prewitt and Scharr in the enhancement stage and the gradient method in the detection stage are used for both verification of the proposed algorithm and comparisons with the existing algorithms. A method with quantitative outcomes is shown to evaluate the performance of the edge detection algorithms. Analysis of the simulations performed on sample images with different operators. The circuit complexity of the algorithm is presented and the comparisons are made with the existing studies. The superiority of the proposed algorithm and its flexibility to be used in other studies are clearly demonstrated by analysis. [ABSTRACT FROM AUTHOR]