Lee, SoonamHan, ShuoSalama, PaulDunn, Kenneth W.Delp, Edward J.2021-01-292021-01-292019Lee, S., Han, S., Salama, P., Dunn, K. W., & Delp, E. J. (2019). Three Dimensional Blind Image Deconvolution for Fluorescence Microscopy using Generative Adversarial Networks. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 538–542. https://doi.org/10.1109/ISBI.2019.8759250https://hdl.handle.net/1805/25082Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy. Fluorescence microscopy image volumes inherently suffer from intensity inhomogeneity, blur, and are corrupted by various types of noise which exacerbate image quality at deeper tissue depth. Therefore, quantitative analysis of fluorescence microscopy in deeper tissue still remains a challenge. This paper presents a three dimensional blind image deconvolution method for fluorescence microscopy using 3way spatially constrained cycle-consistent adversarial networks. The restored volumes of the proposed deconvolution method and other well-known deconvolution methods, denoising methods, and an inhomogeneity correction method are visually and numerically evaluated. Experimental results indicate that the proposed method can restore and improve the quality of blurred and noisy deep depth microscopy image visually and quantitatively.enPublisher Policyimage deconvolutionimage restorationfluorescence microscopyConference proceedings