  Due 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 3-way 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.
