Budi Setiyono

Work place: Department of Mathematics, Institute Teknologi Sepuluh Nopember, Surabaya, Indonesia

E-mail: masbudisetiyono@gmail.com

Website: https://orcid.org/0000-0001-9753-0865

Research Interests:

Biography

Budi Setiyono, he is an Associate Professor at the Faculty of Science and Analytical Data in the Department of Mathematics, Institut Teknologi Sepuluh Nopember (ITS), Indonesia. Bachelor's degree completed in the ITS Mathematics Department. Meanwhile, Masters is from the Departement of Informatics, Institut Teknologi Bandung, Indonesia. He completed a doctoral program at the ITS Electronics Department, with the dissertation title "Superresolution based on Image Sequence using Phase Based Image Matching." He is the secretary of the ITS Mathematics Department in Finance, Resources, and Planning. His current activities are as a lecturer and researcher. His field of research is digital image processing. To date, he has succeeded in publishing several papers on the topic of digital image processing in national and international journals. The research focuses on using digital images and videos to support the Intelligent Transportation System. Some of his published research includes image enhancement, vehicle speed detection, vehicle classification and counting, rain noise removal, road marking violations, and so on. He is also interested in researching quantum-based machine learning methods.

Author Articles
In-depth Study of Quantum Hadamard Gate Edge Detection: Complexity Analysis, Experiments, and Future Directions

By Ridho Nur Rohman Wijaya Budi Setiyono Dwi Ratna Sulistyaningrum

DOI: https://doi.org/10.5815/ijigsp.2025.05.02, Pub. Date: 8 Oct. 2025

Quantum computing is a rapidly developing field with faster computational capabilities than classical computing. The popularity of quantum computing has reached the field of image processing, particularly with a breakthrough method known as Quantum Hadamard Edge Detection. This approach represents a significant advancement in edge detection techniques using quantum computing. Quantum Hadamard Edge Detection is a method that can detect image edges more quickly than classical methods with exponential acceleration. This paper explains the Quantum Hadamard Edge Detection method in detail, including how it is implemented, a time complexity explanation, some experiments, and future research directions. Our experiments utilize a quantum computer simulator and employ four measurement metrics: Structural Similarity Index, Figure of Merit, Entropy, and a Proposed Metric with radius-based features, to detect simple binary images, MNIST images, and the Berkeley Segmentation datasets. We recognize the potential of quantum computing and believe that image processing with quantum representation will make processing more efficient and significantly valuable in the future.

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