Fourier ptychographic microscopy image aberration correction method based on multi-feature fusion residual network

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Abstract
Objective . The study aims to tackle optical aberrations induced by sample non-uniformity or imaging system deviations in Fourier Ptychographic Microscopy (FPM). This technique enables high-resolution microscopic imaging with a wide field of view. The proposed approach involves employing a multi-feature fusion residual network method for aberration correction. This method leverages residual networks and multi-scale convolutional kernels to extract deep residual features and enhance the capacity to capture pertinent image characteristics. Approach . This method optimizes network performance by utilizing grouped convolutions and attention mechanisms without significantly increasing the parameter count. Through the combination of Zernike modes, the accurate estimation and correction of aberrations are achieved, resulting in a substantial enhancement in imaging quality. Main results . The research shows the effective correction of aberrations using this method, facilitating high-resolution image reconstruction while preserving intricate texture details. Validation results, derived from simulated and real images, highlight the efficacy of the method. Significance . By introducing the multi-feature fusion residual network method, this study addresses a significant challenge posed by aberrations in FPM, enhancing imaging quality and presenting a new effective approach for high-resolution microscopic imaging. These results have important implications for fields such as biomedicine, which require stringent image quality standards.
Funding Information
  • Science and Technology Development Plan Projects of Jilin Province ([NO: YDZJ202301ZYTS180])
  • Jilin Provincial Science and Technology Department ([NO:2021JBH02L38])

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