An adaptive soft threshold image denoising method based on quantum bit gate theory

Lu Han; Kun Gao; Yingjie Zhou

doi:10.1117/12.2507067

1 April 2019 An adaptive soft threshold image denoising method based on quantum bit gate theory

Lu Han, Kun Gao, Yingjie Zhou

Proceedings Volume 10752, Applications of Digital Image Processing XLI; 1075236 (2019) https://doi.org/10.1117/12.2507067
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

Because the images are always contaminated by different kinds of noise in the courses of image acquisition, transmission and storage process, the image denoising is a very important step of image restoration. The key of denoising algorithm is making recovery image reserve as much as possible edge details when eliminating noise. Because noise and image details both are part of the high frequency components of image, to some extent, these two sides are contradictory. If the selection of the criterion and treatment for noise and marginal are inappropriate , denoising will make image details ( especially the marginal) become more vague, which must reduce the quality of the image and increase greatly the complexity of subsequent image processing. Since the quantum process and imaging process have the similar characteristics in the probability and statistics fields, a kind of soft threshold denoising algorithm is proposed based on the concept of quantum computation such as the quantum bit, superposition and collapse, etc. This filter algorithm can generate an adaptive template according to the characteristic of the edge of local image. Due to the algorithm is sensitive to the shape of edge, the balance is obtained between the noise suppression and the edge preserving.

Citation Download Citation

Lu Han, Kun Gao, and Yingjie Zhou "An adaptive soft threshold image denoising method based on quantum bit gate theory", Proc. SPIE 10752, Applications of Digital Image Processing XLI, 1075236 (1 April 2019); https://doi.org/10.1117/12.2507067

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KEYWORDS

Denoising

Quantum communications

Quantum computing

Signal to noise ratio

Image quality

Image filtering

Digital filtering

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