Quantum Image Processing

Abstract

This thesis investigates the domain of Quantum Image Processing (QIP), which seeks to enhance classical image processing techniques by leveraging the fundamental principles of quantum mechanics, including superposition, entanglement, and reversibility. The research centers on the Flexible Representation of Quantum Images (FRQI), examining its structure, computational efficiency, and qubit requirements for implementing image-related operations. The study involves the design and simulation of quantum circuits using the Qiskit framework, with a particular focus on the application of quantum filters and the simulation of quantum median filtering techniques for noise reduction in digital images. The experimental findings indicate that quantum-based filtering methods can outperform their classical counterparts in terms of denoising performance, as assessed using standard image quality metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM). Although current quantum hardware remains constrained by factors such as limited qubit availability and intrinsic system noise, the results of this work highlight the promising potential of quantum filtering techniques for future high-performance image processing systems, particularly in the areas of noise suppression and visual enhancement.