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A systematic approach for feature extraction of image based on quantum computing

Vivek Kumar, Madhu Kirola

Published Apr 16, 2026• 0 citations

Abstract

Creating and using quantum algorithms that might perform better than those of conventional computers is the aim of the emerging field of quantum research. QIP has attracted a lot of interest in the last decade. However, the lack of the quantum feature extraction method limits the interpretation of quantum images. FE is the process of extracting relevant details from raw data. Feature extraction is a crucial component of DIP since it significantly affects the accuracy and efficacy of image identification and classification tasks. Features are essential in the field of image processing. It makes it possible for the content of the photographs to be represented as accurately as possible. Before features are obtained, the image is subjected to a variety of image processing procedures, including scaling, filtering, and grey conversion. The features that will be useful for image recognition and classification are then obtained using feature extraction techniques. The purpose of this study was to demonstrate how well the feature extraction technique can be applied to attain high algorithm performance, which in turn improves performance metrics: mean, skewness, contrast, standard deviation, and root mean square error with 125, 4.60, 0.35, 33, and 0.55. This article explains raw data image preprocessing and the quantum feature technique. The results show how effective and superior the proposed method is. We wrap up by providing an overview of this work, pointing out some important discoveries, and outlining potential directions for further research.

DOI: 10.1201/9781003790730-13

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