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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
