This literature review investigates the integration of machine learning (ML) into optical
metrology, unveiling enhancements in both efficiency and effectiveness of measurement …

Phase unwrapping (PU) is a technique to reconstruct original phase images from their noisy
wrapped counterparts finding many applications in scientific imaging. Although supervised …

The speckle noise generated during digital holographic interferometry (DHI) is unavoidable
and difficult to eliminate, thus reducing its accuracy. We propose a self-supervised deep …

Phase unwrapping is a key step to obtain continuous phase distribution in optical phase
measurement. When the wrapped phase obtained from the interference pattern is unclear …

Phase unwrapping (PU) is the process of extracting the authentic phase image from its noisy
wrapped measurements, playing a crucial role in scientific imaging techniques. PU requires …

Speckle noise, mechano-physical noise, and environmental noise are inevitably introduced
in digital holographic coherent imaging, which seriously affects the quality of phase maps …

In this work, we present a high-performance holographic data compression technique. This
approach is based in the temporal correlation found in a holographic video generated using …

Holographic tomography (HT) enables volumetric investigation of biological and
morphological properties of cells and tissues. In its most popular limited-angle …

Phase unwrapping is a crucial step in obtaining the final physical information in the field of
optical metrology. Although good at dealing with phase with discontinuity and noise, most …

Two-dimensional phase unwrapping is a fundamental yet vital task in optical imaging and
measurement. In this paper, what we believe to be a novel deep learning framework …