Advanced high dynamic range fluorescence microscopy with Poisson noise modeling and integrated edge-preserving denoising

In the last decades, fluorescence microscopy has evolved into a powerful tool for modern cell biology and immunology. However, while modern fluorescence microscopes allow to study processes at subcellular level, the informative content of the recorded images is frequently constrained by the limited dynamic range of the camera mounted to the optical system. In addition, the quality of acquired images is generally affected by the typically low-light conditions that lead to comparatively high levels of noise in the data. Addressing these issues, we introduce a variational method for high dynamic range (HDR) imaging in the context of fluorescence microscopy that explicitly accounts for the Poisson statistics of the unavoidable signal-dependent photon shot noise and complements HDR image reconstruction with edge-preserving denoising. Since the proposed model contains a weight function to confine the influence of under- and overexposed pixels on the result, we briefly discuss the choice of this function. We evaluate our approach by showing HDR results for real fluorescence microscopy exposure sequences acquired with the recently developed MACSima(TM) System for fully automated cyclic immunofluorescence imaging. These results are obtained using a first-order primal-dual implementation. On top of this, we also provide the corresponding saddle-point and dual formulations of the problem.

Automated summary

This study addresses the limitations of fluorescence microscopy images caused by the limited dynamic range of the camera and noise. By introducing a variational method that considers Poisson statistics and edge-preserving denoising, high dynamic range imaging in fluorescence microscopy is achieved successfully.