Nonlinear Band-Correlation Guided Deep Reinforcement Learning for Multispectral Autofocus Optimization

Multispectral imaging systems often exhibit wavelength-dependent focus responses because of chromatic aberration, nonuniform spectral sensitivity, and scene-dependent reflectance, making single-band autofocus inadequate for optimizing the complete spectral image cube. This study proposes a nonlinear band-correlation guided deep reinforcement learning framework for autofocus optimization in a self-developed 31-band multispectral imaging system covering 360–980 nm. Through-focus response curves are first extracted from all spectral channels, and a nonlinear inter-band dependency matrix is constructed to characterize complementary and redundant focusing information across wavelengths. A compact subset of representative bands is then selected and embedded into the reinforcement-learning state to guide closed-loop motor control. The agent jointly determines focusing direction, displacement, and stopping time while maximizing a global multispectral focus objective that considers mean sharpness, inter-band consistency, and worst-band degradation. During online autofocus, only the representative bands are acquired at intermediate positions, whereas the complete 31-band cube is captured after convergence. The framework is evaluated using standardized optical targets and spectrally heterogeneous scenes through focal-position error, MTF, full-band focus retention, acquisition time, and robustness. The proposed design provides an efficient computational-photonics solution for broadband multispectral autofocus with reduced band-acquisition and motor-search overhead.