Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer
This work addresses the need for faster and more robust deconvolution in microscopy, but it appears incremental as it builds on existing CNN approaches with specific enhancements.
The paper tackles the problem of computationally expensive blind deconvolution in microscopy by proposing an unsupervised deep neural network with cycle consistency and explicit PSF modeling layers, resulting in improved robustness as confirmed by experimental results.
Deconvolution microscopy has been extensively used to improve the resolution of the widefield fluorescent microscopy. Conventional approaches, which usually require the point spread function (PSF) measurement or blind estimation, are however computationally expensive. Recently, CNN based approaches have been explored as a fast and high performance alternative. In this paper, we present a novel unsupervised deep neural network for blind deconvolution based on cycle consistency and PSF modeling layers. In contrast to the recent CNN approaches for similar problem, the explicit PSF modeling layers improve the robustness of the algorithm. Experimental results confirm the efficacy of the algorithm.