Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation

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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract

Saturation artifacts in optical coherence tomography (OCT) occur when received signal exceeds the dynamic range of spectrometer. Saturation artifact shows a streaking pattern and could impact the quality of OCT images, leading to inaccurate medical diagnosis. In this paper, we automatically localize saturation artifacts and propose an artifact correction method via inpainting. We adopt a dictionary-based sparse representation scheme for inpainting. Experimental results demonstrate that, in both case of synthetic artifacts and real artifacts, our method outperforms interpolation method and Euler's elastica method in both qualitative and quantitative results. The generic dictionary offers similar image quality when applied to tissue samples which are excluded from dictionary training. This method may have the potential to be widely used in a variety of OCT images for the localization and inpainting of the saturation artifacts.

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Keywords
Dictionaries, Training, Machine learning, Optical coherence tomography, Licenses, Interpolation, Image reconstruction, Inpainting, optical coherence tomography, saturation artifacts, sparse representation, Engineering, Electrical & Electronic, Optics, Physics, Applied
Citation
Liu, H., Cao, S., Ling, Y., & Gan, Y. (2021). Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation. In IEEE Photonics Journal (Vol. 13, Issue 2, pp. 1–10). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/jphot.2021.3056574