Noise-robust, deep learning-enhanced dual-wavelength holography for 3D dynamic monitoring of thermosensitive hydrogel...

Accurate 3D dynamic monitoring of thermosensitive hydrogel phase transitions remains challenging due to large deformations and high noise levels. We present a deep learning-enhanced dual-wavelength digital holographic method that overcomes noise amplification in synthetic-wavelength phase maps using an adapted multi-feature fusion unwrapping network, enabling robust phase retrieval under strong speckle noise. The experiments on hydrogels reveal the distinct contraction kinetics below and above the lower critical solution temperature, and a quantitative empirical model linking volume with temperature and time is established. This work provides a precise, wide-range, 3D dynamic measurement platform for soft matter studies and supports the design of hydrogel-integrated adaptive thermal-management systems.