MMM
YYYY
Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-image-free phase retrieval from single-shot hologram
多先验物理增强神经网络实现了单次拍摄全息图的像素超分辨率和双图像无相位检索
多重アプリオリ物理増強ニューラルネットワークは、単一撮影ホログラムの画素超解像度と2画像の位相なし検索を実現した
다중 선험 물리 증강 신경망은 홀로그램을 한 번에 촬영하는 화소 초해상도와 이중 이미지 무위상 검색을 실현한다
La red neuronal de mejora física multiprevia realiza la súper resolución de píxeles de un holograma de disparo único y la recuperación sin fase de imágenes dobles.
Multi - transcendantal Physical Enhanced Neural Networks permet une super - résolution des pixels et une récupération sans phase des images doubles pour un hologramme à prise unique
Многоаприорное физическое усовершенствование нейронной сети обеспечивает пиксельное сверхразрешение одной голограммы и двухфазный поиск изображений
Xuan Tian 田璇 ¹ ², Runze Li 李润泽 ¹, Tong Peng 彭彤 ¹, Yuge Xue 薛雨阁 ¹ ², Junwei Min 闵俊伟 ¹, Xing Li 栗星 ¹, Chen Bai 柏晨 ¹ ², Baoli Yao 姚保利 ¹ ²
¹ State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
中国 西安 中国科学院西安光学精密机械研究所 瞬态光学与光子技术国家重点实验室
² University of Chinese Academy of Sciences, Beijing 100049, China
中国 北京 中国科学院大学
Opto-Electronic Advances, 28 August 2024
Abstract

Digital in-line holographic microscopy (DIHM) is a widely used interference technique for real-time reconstruction of living cells’ morphological information with large space-bandwidth product and compact setup. However, the need for a larger pixel size of detector to improve imaging photosensitivity, field-of-view, and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.

Additionally, the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image. The deep learning (DL) approach has emerged as a powerful tool for phase retrieval in DIHM, effectively addressing these challenges. However, most DL-based strategies are data-driven or end-to-end net approaches, suffering from excessive data dependency and limited generalization ability. Herein, a novel multi-prior physics-enhanced neural network with pixel super-resolution (MPPN-PSR) for phase retrieval of DIHM is proposed. It encapsulates the physical model prior, sparsity prior and deep image prior in an untrained deep neural network.

The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods. With the capabilities of pixel super-resolution, twin-image elimination and high-throughput jointly from a single-shot intensity measurement, the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.
Opto-Electronic Advances_1
Opto-Electronic Advances_2
Opto-Electronic Advances_3
Opto-Electronic Advances_4
Reviews and Discussions
https://www.hotpaper.io/index.html
Genetic algorithm assisted meta-atom design for high-performance metasurface optics
Physics and applications of terahertz metagratings
Surface-patterned chalcogenide glasses with high-aspect-ratio microstructures for long-wave infrared metalenses
Smart photonic wristband for pulse wave monitoring
Multifunctional mixed analog/digital signal processor based on integrated photonics
Three-dimensional multichannel waveguide grating filters
Ka-Band metalens antenna empowered by physics-assisted particle swarm optimization (PA-PSO) algorithm
Optical micro/nanofiber enabled tactile sensors and soft actuators: A review
Photonics-assisted THz wireless communication enabled by wide-bandwidth packaged back-illuminated modified uni-traveling-carrier photodiode
Highly sensitive and real-simultaneous CH4/C2H2 dual-gas LITES sensor based on Lissajous pattern multi-pass cell
Control of light–matter interactions in two-dimensional materials with nanoparticle-on-mirror structures
High performance micromachining of sapphire by laser induced plasma assisted ablation (LIPAA) using GHz burst mode femtosecond pulses



Previous Article                                Next Article
About
|
Contact
|
Copyright © Hot Paper