摘要：Identification of image edges using edge detection is done to obtain
images that are sharp and clear. The selection of the edge detection algorithm will
affect the result. Canny operators have an advantage compared to other edge
detection operators because of their ability to detect not only strong edges but also
weak edges. Until now, Canny edge detection has been done using classical
computing where data are expressed in bits, 0 or 1. This paper proposes the
identification of image edges using a quantum Canny edge detection algorithm,
where data are expressed in the form of quantum bits (qubits). Besides 0 or 1, a
value can also be 0 and 1 simultaneously so there will be many more possible
values that can be obtained. There are three stages in the proposed method, namely
the input image stage, the preprocessing stage, and the quantum edge detection
stage. Visually, the results show that quantum Canny edge detection can detect
more edges compared to classic Canny edge detection, with an average increase of
4.05%.
其他摘要：Identification of image edges using edge detection is done to obtain images that are sharp and clear. The selection of the edge detection algorithm will affect the result. Canny operators have an advantage compared to other edge detection operators because of their ability to detect not only strong edges but also weak edges. Until now, Canny edge detection has been done using classical computing where data are expressed in bits, 0 or 1. This paper proposes the identification of image edges using a quantum Canny edge detection algorithm, where data are expressed in the form of quantum bits (qubits). Besides 0 or 1, a value can also be 0 and 1 simultaneously so there will be many more possible values that can be obtained. There are three stages in the proposed method, namely the input image stage, the preprocessing stage, and the quantum edge detection stage. Visually, the results show that quantum Canny edge detection can detect more edges compared to classic Canny edge detection, with an average increase of 4.05% .
