upU-Net Approaches for Background Emission Removal in Fluorescence Microscopy

Slicing Network for Wide-Field Fluorescence Image Based on the Improved U-Net Model

Fluorescence imaging stands as a pivotal component in biomedical research, requiring the elimination of out-of-focus background noise resulting from wide-field volumetric illumination of the whole field-of-view and scattering within thick biological tissues. Traditional methods struggle to effectively address varying degrees of defocusing in fluorescence images. This study introduces the utilization of upU-Net, 3D U-Net, and 3D upU-Net as defocusing networks tailored for 2D and 3D wide-field fluorescence images, yielding notable enhancements. These advancements facilitate more economically viable confocal microscopy, delivering significant advantages to biologists presently utilizing wide-field fluorescence microscopy.

Constrained Plug-and-Play Priors for Image Restoration

The Plug-and-Play framework has demonstrated that a denoiser can implicitly serve as the image prior for model-based methods for solving various inverse problems such as image restoration tasks. This characteristic enables the integration of the flexibility of model-based methods with the effectiveness of learning-based denoisers. However, the regularization strength induced by denoisers in the traditional Plug-and-Play framework lacks a physical interpretation, necessitating demanding parameter tuning. This paper addresses this issue by introducing the Constrained Plug-and-Play (CPnP) method, which reformulates the traditional PnP as a constrained optimization problem. In this formulation, the regularization parameter directly corresponds to the amount of noise in the measurements. The solution to the constrained problem is obtained through the design of an efficient method based on the Alternating Direction Method of Multipliers (ADMM). Our experiments demonstrate that CPnP outperforms competing methods in terms of stability and robustness while also achieving competitive performance for image quality.