A quantum mechanics-based framework for image processing and its application to image segmentation

Quantum mechanics provides the physical laws governing microscopic systems. A novel and generic framework based on quantum mechanics for image processing is proposed in this paper. The basic idea is to map each image element to a quantum system. This enables the utilization of the quantum mechanics powerful theory in solving image processing problems. The initial states of the image elements are evolved to the final states, controlled by an external force derived from the image features. The final states can be designed to correspond to the class of the element providing solutions to image segmentation, object recognition, and image classification problems. In this work, the formulation of the framework for a single-object segmentation problem is developed. The proposed algorithm based on this framework consists of four major steps. The first step is designing and estimating the operator that controls the evolution process from image features. The states associated with the pixels of the image are initialized in the second step. In the third step, the system is evolved. Finally, a measurement is performed to determine the output. The presented algorithm is tested on noiseless and noisy synthetic images as well as natural images. The average of the obtained results is 98.5 % for sensitivity and 99.7 % for specificity. A comparison with other segmentation algorithms is performed showing the superior performance of the proposed method. The application of the introduced quantum-based framework to image segmentation demonstrates high efficiency in handling different types of images. Moreover, it can be extended to multi-object segmentation and utilized in other applications in the fields of signal and image processing.