Adaptive blind deconvolution for fluorescence microscopy via sparse prior

Adaptive optics (AO) is becoming a valuable tool for high resolution microscopy, providing correction for aberrations introduced by the refractive index structure of specimens. However, traditional AO methods typically rely on additional hardware to measure and correct system aberrations, which can be both complex and costly. Here, we introduce a computational adaptive optics technique based on sparse blind deconvolution (CAO-SBD), which uses a single blurred image to estimate aberrations and perform image deblurring. By incorporating sparse priors of fluorescent specimens with Zernike polynomial-based aberration modeling, CAO-SBD allows for the simultaneous reconstruction of both the aberrated point spread function (PSF) and the sample information, eliminating the need for precise PSF calibration. Our method outperforms traditional Richardson-Lucy deconvolution by enhancing robustness to noise and stabilizing the deconvolution process through adaptive PSF correction. Experimental results demonstrate that CAO-SBD significantly enhances image resolution and contrast across both wide-field and confocal fluorescence microscopic systems, positioning CAO-SBD as a powerful tool for high-resolution biological imaging with broad applications.