12665

Quantum Boolean Image Denoising

Mario Mastriani
DLQS LLC, 4431 NW 63RD Drive, Coconut Creek, FL 33073, USA
arXiv:1408.2427 [quant-ph], (11 Aug 2014)
@article{2014arXiv1408.2427M,

   author={Mastriani}, M.},

   title={"{Quantum Boolean Image Denoising}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1408.2427},

   primaryClass={"quant-ph"},

   keywords={Quantum Physics},

   year={2014},

   month={aug},

   adsurl={http://adsabs.harvard.edu/abs/2014arXiv1408.2427M},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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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.
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