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

Abstract

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. We use a unified formulation that comprehensively takes advantage of multiple compressed sensing reconstructions and blind sparse representation. 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. Accordingly, the proposed method can suppress the noise level, enhance the SNR, and recover the degraded resolution simultaneously. The obtained results demonstrate the proposed CBDD algorithm is well suited to improve the imaging performance of the CBS light-sheet fluorescence microscopy.