Wavefront Sensing using GMM Clustering for Initialization of L-BFGS Optimization - Goel, Neha; Ganotra, Dinesh (Discontinuity, Nonlinearity, and Complexity, 2026)
This paper introduces a method for correcting wavefront aberrations using intensity images obtained under varying phase diversities. The proposed methodology integrates Gaussian mixture model (GMM) clustering and employs the resulting cluster centers as initial positions for a Limited memory Broyden Fletcher Goldfarb Shanno (L-BFGS) optimization process. The study evaluates the performance of this method using simulated data. The dataset consists of 500 distinct aberrations characterized by root mean square (RMS) errors ranging from 0.2λ to 0.3λ. The analysis focuses on assessing the accuracy achieved, particularly emphasizing the effectiveness of wavefront reconstruction. The obtained RMS residual errors range from 0.017λ (lowest) to 0.066λ (highest), with an average of 0.039λ. Notably, 89.6% of the RMS residual errors fall below 0.05λ. These results demonstrate the reliability and practicality of the proposed approach in wavefront aberration correction, as confirmed through numerical experimentation. Results can improve applications requiring precise wavefront correction for clear and detailed observations, such as astronomical imaging or high-resolution microscopy.