Compressed Blind Deconvolution and Denoising for Complementary Beam Subtraction Light-Sheet Fluorescence Microscopy

Objective: The side-lobes of a Bessel beam (BB) create a severe out-of-focus background in scanning light-sheet fluorescence microscopy, thereby extremely limiting the axial resolution. The complementary beam subtraction (CBS) method can significantly reduce the out-of-focus background by double scanning a BB and its complementary beam. However, the blurring and noise caused by the system instability during the double scanning and subtraction operations degrade the image quality significantly. Therefore, we propose a compressed blind deconvolution and denoising (CBDD) method that solves this problem. Methods: We use a unified formulation that comprehensively takes advantage of multiple compressed sensing reconstructions and blind sparse representation. Results: The simulations and experiments were performed using the microbeads and model organisms to verify the effectiveness of the proposed method. Compared with the CBS light-sheet method, the proposed CBDD algorithm achieved the gain improvement in the axial and lateral resolution of about 1.81 and 2.22 times, respectively, while the average signal-to-noise ratio (SNR) was increased by about 3 dB. Conclusion: Accordingly, the proposed method can suppress the noise level, enhance the SNR, and recover the degraded resolution simultaneously. Significance: The obtained results demonstrate the proposed CBDD algorithm is well suited to improve the imaging performance of the CBS light-sheet fluorescence microscopy.

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