An image despeckling approach using quantum-inspired statistics in dual-tree complex wavelet domain. (April 2015)

• Record Type: Journal Article
• Authors: Fu, Xiaowei; Wang, Yi; Chen, Li; Tian, Jing
• Is Part Of: Biomedical signal processing and control. Volume 18(2015)
• Page Start: 30
• Page End: 35
• Publication Date: 2015-04
• Subjects: Ultrasound image -- Despeckling -- Quantum signal processing; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2014.11.005
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We apply quantum theory on image despeckling problem. We use both inter-scale and intra-scale characteristics of wavelet coefficients. We propose a novel QSP-based thresholding function. We achieve better performance on simulated and in vivo medical ultrasound images. Abstract: This paper presents a novel despeckling algorithm to enhance image quality of medical ultrasound images. The proposed approach exploits coefficients in the dual-tree complex wavelet transform (DTCWT) domain to develop a new quantum-inspired thresholding function. The inter-scale correlation among the coefficients of different subbands and the intra-scale variance between the coefficient and its neighborhood in the same subband are utilized to develop a new thresholding function, which is further incorporated into a Bayesian framework to perform adaptive image despeckling. Experiments are conducted using both artificially generated and real-world medical images to demonstrate that the proposed approach outperforms the conventional image despeckling approaches.