Wang Lu, Liu Yuxiang, Meng Fanxu, Luan Tian, Liu Wenjie, Zhang Zaichen, Yu Xutao
School of Information Science and Engineering, Southeast University, No.2, Southeast University Road, Nanjing 211189, Jiangsu, China.
State Key Laboratory of Millimeter Waves, Southeast University, No.2, Southeast University Road, Nanjing 211189, Jiangsu, China.
iScience. 2024 Apr 1;27(5):109627. doi: 10.1016/j.isci.2024.109627. eCollection 2024 May 17.
The quantum denoising technology efficiently removes noise from images; however, the existing algorithms are only effective for additive noise and cannot remove multiplicative noise, such as speckle noise in synthetic aperture radar (SAR) images. In this paper, based on the grayscale morphology method, a quantum SAR image denoising algorithm is proposed, which performs morphological operations on all pixels simultaneously to remove the noise in the SAR image. In addition, we design a feasible quantum adder to perform cyclic shift operations. Then, quantum circuits for dilation and erosion are designed, and the complete quantum circuit is then constructed. For a quantum SAR image with grayscale levels, the complexity of our algorithm is O . Compared with classical algorithms, it achieves exponential improvement and also has polynomial-level improvements than existing quantum algorithms. Finally, the feasibility of our algorithm is validated on IBM Q.
量子去噪技术能有效去除图像噪声；然而，现有算法仅对加性噪声有效，无法去除乘性噪声，如合成孔径雷达（SAR）图像中的斑点噪声。本文基于灰度形态学方法，提出一种量子SAR图像去噪算法，该算法对所有像素同时进行形态学运算以去除SAR图像中的噪声。此外，我们设计了一种可行的量子加法器来执行循环移位操作。然后，设计了膨胀和腐蚀的量子电路，并构建了完整的量子电路。对于具有灰度级的量子SAR图像，我们算法的复杂度为O。与经典算法相比，它实现了指数级的提升，并且比现有的量子算法也有多项式级别的提升。最后，在IBM Q上验证了我们算法的可行性。
