Optical wavefront aberration: detection, recognition, and compensation techniques – a comprehensive review

Abstract

Wavefront aberration sensors are essential tools in optical systems, enabling accurate measurement and correction of deviations from ideal wavefront shapes. This review provides a comprehensive overview of the key wavefront sensing technologies, including interferometry, Pyramid wavefront sensors, Shack-Hartmann wavefront sensors, and aberration-matched filters. Each sensor type is examined with respect to its underlying principles, practical applications, and performance characteristics. Greater emphasis is placed on the detailed comparison between the classic Shack-Hartmann wavefront sensor, the pyramid wavefront sensor employing a refractive optical element, and aberration-matched filters utilizing diffractive optics. The integration of diffractive optics in matched filtering plays a pivotal role in enhancing the system’s performance, enabling the effective application of machine learning techniques for accurate detection and compensation of optical aberrations. The paper also explores the transformative role of artificial intelligence in advancing these technologies, highlighting how machine learning and deep learning techniques are being applied to enhance sensor accuracy, robustness, and adaptability. Additionally, the limitations and challenges associated with each type of sensor, such as cost, resolution constraints, and sensitivity to environmental conditions, are discussed. By addressing both traditional and AI-enhanced wavefront sensing methods, this review aims to provide insights into current trends and future directions in wavefront aberration measurement and correction.