A quantum synthetic aperture radar image denoising algorithm based on grayscale morphology
Lu Wang,
Yuxiang Liu,
Fanxu Meng,
Tian Luan,
Wenjie Liu,
Zaichen Zhang,
Xutao Yu
Affiliations
Lu Wang
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; Quantum Information Center, Southeast University, No.2, Southeast University Road, Nanjing 211189, Jiangsu, China
Yuxiang Liu
School of Information Science and Engineering, Southeast University, No.2, Southeast University Road, Nanjing 211189, Jiangsu, China; Quantum Information Center, Southeast University, No.2, Southeast University Road, Nanjing 211189, Jiangsu, China; National Mobile Communications Research Laboratory, Southeast University, No.2, Southeast University Road, Nanjing 211189, Jiangsu, China
Fanxu Meng
College of Artificial Intelligence, Nanjing Tech University, No.30, Puzhu Nan Road, Nanjing 211800, Jiangsu, China
Tian Luan
Yangtze Delta Region Industrial Innovation Center of Quantum and Information Technology, No.286, Qinglong Gang Road, Suzhou 215100, Jiangsu, China
Wenjie Liu
School of Software, Nanjing University of Information Science and Technology, No.219, Ning Liu Road, Nanjing 210044, Jiangsu, China
Zaichen Zhang
School of Information Science and Engineering, Southeast University, No.2, Southeast University Road, Nanjing 211189, Jiangsu, China; Quantum Information Center, Southeast University, No.2, Southeast University Road, Nanjing 211189, Jiangsu, China; National Mobile Communications Research Laboratory, Southeast University, No.2, Southeast University Road, Nanjing 211189, Jiangsu, China; Purple Mountain Laboratories, No.9, Mozhou Dong Road, Nanjing 211111, Jiangsu, China
Xutao Yu
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; Quantum Information Center, Southeast University, No.2, Southeast University Road, Nanjing 211189, Jiangsu, China; Purple Mountain Laboratories, No.9, Mozhou Dong Road, Nanjing 211111, Jiangsu, China; Corresponding author
Summary: 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 2n×2n quantum SAR image with q grayscale levels, the complexity of our algorithm is O (n+q). 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.
