A fusion denoising method based on homomorphic transform and 3D transform-domain collaborative filtering for laser speckle imaging of blood flow. Issue 19 (11th November 2022)

• Record Type: Journal Article
• Authors: Fu, Xuenian; Wu, Sijin; Si, Juanning; Li, Weixian
• Is Part Of: Journal of modern optics. Volume 69:Issue 19(2022)
• Page Start: 1103
• Page End: 1114
• Publication Date: 2022-11-11
• Subjects: Laser speckle contrast imaging -- homomorphic transform -- block-matching and 3D transform-domain collaborative filtering -- image contrast; Optics -- Periodicals; 535
• DOI: 10.1080/09500340.2022.2146224
• Languages: English
• ISSNs: 0950-0340

Abstract

Abstract : Laser speckle contrast imaging (LSCI) is a real-time, full-field, non-contact optical imaging technique that has been widely used in blood flow imaging. However, its practical applications are limited due to its low signal-to-noise ratio (SNR) and low image contrast. In this study, a fusion denoising algorithm based on homomorphic transform and block-matching and 3D transform-domain collaborative filtering algorithm is proposed to reduce noise and improve image contrast. The reliability and applicability of the proposed method were evaluated by a phantom experiment and two in vivo experiments. The results show that the proposed algorithm can effectively remove the noise and enhance the image contrast. Consequently, the SNR is increased, the dynamic range of LSCI is expanded, and the estimation accuracy of blood flow is improved.