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Edge detection is a fundamental task in digital image processing. Marr–Hildreth edge detection is the basic tool for implementing edge detection in classic image processing. This paper studies the quantum version of the classical Marr–Hildreth edge detection, which includes two core processes: Gaussian–Laplacian filtering and zero-crossing extraction. Based on the sampling results of the Gaussian–Laplace function, Gaussian–Laplacian filtering is implemented directly using quantum multipliers and quantum adders. Zero-crossing extraction is achieved using several quantum comparators. The quantum circuits of these two core processes with several auxiliary operators are designed in detail. Complexity analysis shows that the quantum Marr–Hildreth edge detection has exponential speedup compared to its classical counterpart. The simulation on the classical computer verifies the correctness of the quantum Marr–Hildreth edge detection results. [ABSTRACT FROM AUTHOR]

In this paper, a novel quantum representation and watermarking scheme based on the controlled rotation of qubits are proposed. Firstly, a flexible representation for quantum color image (FRQCI) is proposed to facilitate the image processing tasks. Some basic image processing operations based on FRQCI representation are introduced. Then, a novel watermarking scheme for quantum images is presented. In our scheme, the carrier image is stored in the phase $$\theta $$ of a qubit; at the same time, the watermark image is embedded into the phase $$\phi $$ of a qubit, which will not affect the carrier image's visual effect. Before being embedded into the carrier image, the watermark image is scrambled to be seemingly meaningless using quantum circuits, which further ensures the security of the watermark image. All the operations mentioned above are implemented by the controlled rotation of qubits. The experimental results on the classical computer show that the proposed watermarking scheme has better visual quality under a higher embedding capacity and outperforms the existing schemes in the literature. [ABSTRACT FROM AUTHOR]

Morphological image processing is a relatively mature image processing method in classical image processing. However, in quantum image processing, the related results are still quite scarce. In this paper, we first design the quantum circuits of the two basic operations of dilation and erosion for binary images and grayscale images. On this basis, for binary image, the quantum circuits of three morphological algorithms (noise removal, boundary extraction and skeleton extraction) are designed in detail. For grayscale image, the quantum circuits of three morphological algorithms (i.e., edge detection, image enhancement and texture segmentation) are also designed. In the design of these circuits, the parallelism of quantum computation is considered. The analysis of the circuits complexity shows that all the six morphological algorithms can speed up their classic counterparts. [ABSTRACT FROM AUTHOR]