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arXiv:1408.2427

Quantum Boolean Image Denoising

Mario Mastriani

Published Aug 11, 2014• 0 citations
arXivHTMLPDF

Abstract

A quantum Boolean image processing methodology is presented in this work, with special emphasis in image denoising. A new approach for internal image representation is outlined together with two new interfaces: classical-to-quantum and quantum-to-classical. The new quantum-Boolean image denoising called quantum Boolean mean filter (QBMF) works with computational basis states (CBS), exclusively. To achieve this, we first decompose the image into its three color components, i.e., red, green and blue. Then, we get the bitplanes for each color, e.g., 8 bits-per-pixel, i.e., 8 bitplanes-per-color. From now on, we will work with the bitplane corresponding to the most significant bit (MSB) of each color, exclusive manner. After a classical-to-quantum interface (which includes a classical inverter), we have a quantum Boolean version of the image within the quantum machine. This methodology allows us to avoid the problem of quantum measurement, which alters the results of the measured except in the case of CBS. Said so far is extended to quantum algorithms outside image processing too. After filtering of the inverted version of MSB (inside quantum machine) the result passes through a quantum-classical interface (which involves another classical inverter) and then proceeds to reassemble each color component and finally the ending filtered image. Finally, we discuss the more appropriate metrics for image denoising in a set of experimental results.

DOI
10.48550/arxiv.1408.2427

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Quantum image processing is paramount in image processing-based research areas nowadays due to the less computational time. This paper presents the quantum mechanic-based image denoising model. Also, This article discusses the quantum boolean mean filter and new quantum-boolean image denoising that are connected to computational basis states. This paper describes basic quantum information processing principles, new and better image representation approaches, quantum image processing implementation problems, quantum boolean mean filtering approach for image denoising observation using appropriate matrice, and finally, a work proposal for future work and conclusion. A comparison table of different classification techniques is also included in the paper.
This paper addresses the problem of digital image denoising using a median filtering method based on concepts inspired by quantum computing. In the proposed approach, pixel values are encoded in a manner analogous to quantum states, and their distribution is processed using the Quantum Fourier Transform. The median value estimation is performed based on a probabilistic distribution, which enables a direct comparison with the classical median filtering technique. Artificial noise is added to the images, and the quality of the images before and after filtering is evaluated using objective quality metrics. The experimental results demonstrate that the proposed quantum median filtering model provides a positive effect in restoring image quality and effectively reduces the impact of noise. This approach highlights the potential of applying quantum-inspired computational methods in the field of digital image processing.
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