Superresolution optical fluctuation imaging (SOFI) is an attractive and affordable alternative to more established superresolution imaging methods. It provides moderate resolution enhancement and an efficient estimation of the optical point spread function (PSF). Moreover, further resolution enhancement could be achieved by deconvolution of the SOFI image. In this paper, we propose a novel image deconvolution approach based on the shearlet transform and the fractional-order total variation (FOTV) to further improve SOFI images. Since SOFI PSF estimation is imperfect in practice, we also propose a prior-guided semi-blind deconvolution method. Numerical experiments on simulated images with microtubule-like structures have shown that our proposed algorithms can recover filamentous features with high accuracy and outperforms other state-of-the-art deconvolution methods.
|Title of host publication||2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018|
|Number of pages||4|
|State||Published - May 23 2018|
|Event||15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States|
Duration: Apr 4 2018 → Apr 7 2018
|Name||Proceedings - International Symposium on Biomedical Imaging|
|Conference||15th IEEE International Symposium on Biomedical Imaging, ISBI 2018|
|Period||4/4/18 → 4/7/18|
Bibliographical noteFunding Information:
The work from S. Weiss and X. Yi was supported by Dean Willard Chair fund and STROBE: A National Science Foundation Science & Technology Center under Grant No. DMR 1548924.
© 2018 IEEE.
- Alternating direction method of multipliers
- Alternating minimization algorithm
- Fractional-order total variation
- Image deconvolution
- Shearlet transform
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging