Company Filing History:
Years Active: 2025
Title: Hanlong Chen - Innovator in Deep Learning and Holography
Introduction
Hanlong Chen is a prominent inventor based in Los Angeles, CA. He has made significant contributions to the field of deep learning and holography. His innovative work focuses on enhancing image reconstruction techniques through advanced neural network architectures.
Latest Patents
Hanlong Chen holds a patent for a groundbreaking invention titled "Deep neural network for hologram reconstruction with superior external generalization." This patent introduces a deep learning framework known as the Fourier Imager Network (FIN). The FIN is designed to perform end-to-end phase recovery and image reconstruction from raw holograms of new types of samples. It demonstrates remarkable success in external generalization, making it a valuable tool in various imaging applications. The architecture of FIN is based on spatial Fourier transform modules, which utilize learnable filters and a global receptive field to process spatial frequencies. This innovative approach allows FIN to achieve superior generalization to new sample types while significantly improving image inference speed, completing hologram reconstruction tasks in approximately 0.04 seconds per 1 mm of the sample area.
Career Highlights
Hanlong Chen is affiliated with the University of California, where he continues to advance research in computational imaging and machine vision. His work not only enhances holographic microscopy and quantitative phase imaging but also opens new avenues for designing broadly generalizable deep learning models.
Collaborations
Hanlong Chen has collaborated with notable researchers in his field, including Aydogan Ozcan and Luzhe Huang. Their combined expertise contributes to the advancement of innovative technologies in imaging and deep learning.
Conclusion
Hanlong Chen's contributions to deep learning and holography exemplify the potential of innovative technologies in transforming imaging applications. His work continues to inspire advancements in computational imaging and machine vision fields.