In response to the emerging demand for dynamic and cross-scale microscopic observation in fields such as biology, medicine, and materials science, the liquid lens has been widely adopted in modern microscopy to enable real-time, continuous optical zooming. However, the limited optical power of the liquid lens restricts the zoom range, and the nonlinear dynamic aberrations introduced during zooming can significantly degrade image quality. To address these challenges, a continuous optical zoom microscope with a large zoom ratio and adaptive aberration correction is proposed, based on an end-to-end joint optimization framework that integrates optical design and neural network guided by physical degradation. By incorporating spatially-variant, multi-wavelength, and continuous-magnification 4D PSF of the system as physical priors, this work achieves fast and high-quality continuous zoom imaging from 10.6× ~ 101.4×, while adaptively correcting complex aberrations that vary with both magnification and spatial location. The core hardware component of the system is a zoom objective lens with a movable relay image plane, which especially integrates electrowetting liquid lenses. On the algorithmic side, 4DPSF-aware Physical Degradation-guided Network (4DPSF-PDNet) is introduced for adaptive aberration correction during the zooming process. By embedding the PSF into the network, the method effectively suppresses distortions and artifacts and achieves precise correction of complex, dynamically varying aberrations. Experimental results demonstrate that the proposed adaptive continuous microscope holds significant promise for a wide range of applications in biology, medical diagnostics, and materials science.
Yu et al. (2026) studied this question.