Abstract
Quantum image processing reduces the gap between quantum computing and image processing fields. The principles of quantum computing explore the image processing in various ways of handling (capture, manipulate, extract) images of different formats and for different purposes. In this paper, an image denoising scheme based on quantum wavelet transform (QWT) is proposed. Initially, a quantum noisy image is formed with the help of geometric transformation operation and embedded into the wavelet coefficients of the quantum original grayscale image. As a result, it will affect the visual quality of the original quantum image. Then the quantum Daubechies wavelet kernel of fourth order is used to extract wavelet coefficients from the resultant image. Then we consider a Quantum Oracle that implements a suitable thresholding function to decompose the wavelet coefficients into a greater effect applicable for the original image wavelet coefficients and lower effect for the noisy image wavelet coefficients. However, original image wavelet coefficients are greater than the noisy wavelet coefficients. A detail computational time and storage complexity analysis is given and compared with some state-of-the-art denoising techniques at the end of this paper.
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Chakraborty, S., Shaikh, S.H., Chakrabarti, A., Ghosh, R. (2020). A Study of Scrambled Noisy Quantum Image Formation with Geometric Transformation and Its Denoising Using QWT. In: Nanda, A., Chaurasia, N. (eds) High Performance Vision Intelligence. Studies in Computational Intelligence, vol 913. Springer, Singapore. https://doi.org/10.1007/978-981-15-6844-2_10
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DOI: https://doi.org/10.1007/978-981-15-6844-2_10
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