Deblurring, artifact-free optical coherence tomography with deconvolution-random phase modulation
반권적 랜덤 위상 변조 모호 무위영 광학 상간 계층 분석 영상
Deconvolución modulación de fase aleatoria deconvolución sin artefactos tomografía de coherencia óptica
Déconvolution modulation de phase aléatoire décontextualisée sans artefacts tomographie par cohérence optique
Обратная свертка стохастическая фазовая модуляция
Xin Ge 葛昕 ¹, Si Chen 陈思 ¹, Kan Lin 林侃 ¹, Guangming Ni 倪光明 ⁴, En Bo 伯恩 ¹, Lulu Wang 王露露 ¹, Linbo Liu 刘琳波 ¹ ² ³
¹ School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
² School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459, Singapore
³ China-Singapore International Joint Research Institute (CSIJRI), Guangzhou 510000, China
中国 广州 中新国际联合研究院(CSIJRI)
⁴ School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
中国 成都 电子科技大学 光电科学与工程学院
Opto-Electronic Science, 31 January 2024

Deconvolution is a commonly employed technique for enhancing image quality in optical imaging methods. Unfortunately, its application in optical coherence tomography (OCT) is often hindered by sensitivity to noise, which leads to additive ringing artifacts. These artifacts considerably degrade the quality of deconvolved images, thereby limiting its effectiveness in OCT imaging.

In this study, we propose a framework that integrates numerical random phase masks into the deconvolution process, effectively eliminating these artifacts and enhancing image clarity. The optimized joint operation of an iterative Richardson-Lucy deconvolution and numerical synthesis of random phase masks (RPM), termed as Deconv-RPM, enables a 2.5-fold reduction in full width at half-maximum (FWHM).

We demonstrate that the Deconv-RPM method significantly enhances image clarity, allowing for the discernment of previously unresolved cellular-level details in nonkeratinized epithelial cells ex vivo and moving blood cells in vivo.
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