Self-supervised transformer based non-local means despeckling of optical coherence tomography images. (February 2023)

• Record Type: Journal Article
• Authors: Zhou, Quan; Wen, Mingwei; Yu, Bin; Lou, Cuijuan; Ding, Mingyue; Zhang, Xuming
• Is Part Of: Biomedical signal processing and control. Volume 80:Part 2(2023)
• Publication Date: 2023-02
• Subjects: Optical coherence tomography -- Non-local means -- Self-supervised transformer -- Boosting strategy -- OCT image despeckling; 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.2022.104348
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Optical coherence tomography (OCT) depends on the coherence characteristics of scattered light to reveal tissue morphology. Therefore, OCT images are inevitably corrupted by speckle noise. The non-local means (NLM) method is a popular method for OCT image despeckling. However, the existing NLM algorithm cannot preserve image details well or deliver sufficient noise reduction because they calculate the similarity weights based on the grayscale information or human-designed features of an image. This letter presents a self-supervised transformer based NLM method for despeckling OCT images. The presented method computes the weight of the NLM using the deep features extracted by the self-supervised transformer and adopts the boosting strategy to realize the effective OCT image despeckling. The experiments on two OCT image datasets demonstrate that our algorithm performs better than other compared denoising algorithms in terms of the quantitative metrics and visual evaluation.