Wang Shumei, Xu Pengao, Song Ruicheng, Li Peiyao, Ma Hongyang
Quantum Physics Laboratory, School of Sciences, Qingdao University of Technology, Qingdao 266520, China.
Entropy (Basel). 2020 Oct 25;22(11):1207. doi: 10.3390/e22111207.
Recent development of computer technology may lead to the quantum image algorithms becoming a hotspot. Quantum information and computation give some advantages to our quantum image algorithms, which deal with the limited problems that cannot be solved by the original classical image algorithm. Image processing cry out for applications of quantum image. Most works on quantum images are theoretical or sometimes even unpolished, although real-world experiments in quantum computer have begun and are multiplying. However, just as the development of computer technology helped to drive the Technology Revolution, a new quantum image algorithm on constrained least squares filtering computation was proposed from quantum mechanics, quantum information, and extremely powerful computer. A quantum image representation model is introduced to construct an image model, which is then used for image processing. Prior knowledge is employed in order to reconstruct or estimate the point spread function, and a non-degenerate estimate is obtained based on the opposite processing. The fuzzy function against noises is solved using the optimal measure of smoothness. On the constraint condition, determine the minimum criterion function and estimate the original image function. For some motion blurs and some kinds of noise pollutions, such as Gaussian noises, the proposed algorithm is able to yield better recovery results. Additionally, it should be noted that, when there is a noise attack with very low noise intensity, the model based on the constrained least squares filtering can still deliver good recovery results, with strong robustness. Subsequently, discuss the simulation analysis of the complexity of implementing quantum circuits and image filtering, and demonstrate that the algorithm has a good effect on fuzzy recovery, when the noise density is small.
计算机技术的最新发展可能会使量子图像算法成为一个热点。量子信息与计算为我们的量子图像算法带来了一些优势，这些算法可以处理一些原始经典图像算法无法解决的有限问题。图像处理迫切需要量子图像的应用。尽管量子计算机的实际实验已经开始并且正在不断增加，但大多数关于量子图像的研究都是理论性的，有时甚至还不完善。然而，正如计算机技术的发展推动了技术革命一样，从量子力学、量子信息以及功能极其强大的计算机出发，提出了一种关于约束最小二乘滤波计算的新量子图像算法。引入了量子图像表示模型来构建图像模型，然后将其用于图像处理。利用先验知识来重构或估计点扩散函数，并基于反向处理获得非退化估计。使用最优平滑度量来求解抗噪声的模糊函数。在约束条件下，确定最小准则函数并估计原始图像函数。对于一些运动模糊和某些类型的噪声污染，如高斯噪声，该算法能够产生更好的恢复结果。此外，需要注意的是，当存在噪声强度非常低的噪声攻击时，基于约束最小二乘滤波的模型仍然可以提供良好的恢复结果，具有很强的鲁棒性。随后，讨论了实现量子电路和图像滤波复杂性的仿真分析，并证明当噪声密度较小时，该算法在模糊恢复方面具有良好的效果。
