Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks
This addresses the challenge of quantitative analysis in fluorescence microscopy for deeper tissue imaging, though it appears incremental as it builds on existing deconvolution and adversarial network techniques.
The paper tackles the problem of image blurring and noise in deep tissue fluorescence microscopy by proposing a 3D blind image deconvolution method using generative adversarial networks, which restores image quality with visual and quantitative improvements.
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.