Wavelet Estimation of Function Derivatives from a Multichannel Deconvolution Model DOI Creative Commons
Huijun Guo,

Shuzi Li

Journal of Function Spaces, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 11

Published: Dec. 31, 2022

This paper considers a multichannel deconvolution model with Gaussian white noises. The goal is to estimate the d -th derivatives of an unknown function in model. For super-smooth case, we construct adaptive linear wavelet estimator by projection method. regular-smooth provide nonlinear hard-thresholded In order measure global performances our estimators, show upper bounds on convergence rates using L p -risk ( 1 p < ).

Language: Английский

On the use of deep learning for phase recovery DOI Creative Commons
Kaiqiang Wang, Li Song, Chutian Wang

et al.

Light Science & Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: Jan. 1, 2024

Phase recovery (PR) refers to calculating the phase of light field from its intensity measurements. As exemplified quantitative imaging and coherent diffraction adaptive optics, PR is essential for reconstructing refractive index distribution or topography an object correcting aberration system. In recent years, deep learning (DL), often implemented through neural networks, has provided unprecedented support computational imaging, leading more efficient solutions various problems. this review, we first briefly introduce conventional methods PR. Then, review how DL provides following three stages, namely, pre-processing, in-processing, post-processing. We also used in image processing. Finally, summarize work provide outlook on better use improve reliability efficiency Furthermore, present a live-updating resource ( https://github.com/kqwang/phase-recovery ) readers learn about

Language: Английский

Citations

77

Deep learning assisted non-contact defect identification method using diffraction phase microscopy DOI

S. Narayan,

Allaparthi Venkata Satya Vithin, Rajshekhar Gannavarpu

et al.

Applied Optics, Journal Year: 2023, Volume and Issue: 62(20), P. 5433 - 5433

Published: June 20, 2023

Reliable detection of defects from optical fringe patterns is a crucial problem in non-destructive interferometric metrology. In this work, we propose deep-learning-based method for pattern defect identification. By attributing the information to pattern's phase gradient, compute spatial derivatives using deep learning model and apply gradient map localize defect. The robustness proposed illustrated on multiple numerically synthesized at various noise levels. Further, practical utility substantiated experimental identification diffraction microscopy.

Language: Английский

Citations

9

Simultaneous estimation of multiple order phase derivatives using deep learning method in digital holographic interferometry DOI

S. Narayan,

Rajshekhar Gannavarpu

Optics and Lasers in Engineering, Journal Year: 2024, Volume and Issue: 184, P. 108583 - 108583

Published: Sept. 13, 2024

Language: Английский

Citations

3

人工智能定量相位成像:从物理到算法再到物理(内封面文章·特邀) DOI

田璇 TIAN Xuan,

费舒全 FEI Shuquan,

李润泽 LI Runze

et al.

Infrared and Laser Engineering, Journal Year: 2025, Volume and Issue: 54(2), P. 20240490 - 20240490

Published: Jan. 1, 2025

Citations

0

Correction of spurious phase sign in single closed-fringe demodulation using transformer based Swin-ResUnet DOI Open Access

Yu Kuang,

Fengwei Liu,

Yuanchao Liu

et al.

Optics & Laser Technology, Journal Year: 2023, Volume and Issue: 168, P. 109952 - 109952

Published: Aug. 24, 2023

Language: Английский

Citations

7

Deep learning based single shot multiple phase derivative retrieval method in multi-wave digital holographic interferometry DOI
Allaparthi Venkata Satya Vithin, Jagadesh Ramaiah, Rajshekhar Gannavarpu

et al.

Optics and Lasers in Engineering, Journal Year: 2022, Volume and Issue: 162, P. 107442 - 107442

Published: Dec. 22, 2022

Language: Английский

Citations

11

Fringe pattern normalization using conditional Generative Adversarial Networks DOI

Viren S Ram,

Rajshekhar Gannavarpu

Optik, Journal Year: 2024, Volume and Issue: 313, P. 171999 - 171999

Published: Aug. 13, 2024

Language: Английский

Citations

2

Deep learning assisted state space method for phase derivative estimation in digital holographic interferometry DOI Creative Commons
Dhruvam Pandey, Rajshekhar Gannavarpu

Optics Continuum, Journal Year: 2024, Volume and Issue: 3(9), P. 1765 - 1765

Published: Sept. 4, 2024

In digital holographic interferometry, the measurement of derivatives interference phase plays a crucial role in deformation testing since displacement corresponding to deformed object are directly related derivatives. this work, we propose recurrent neural network-assisted state space method for reliable estimation The proposed offers high robustness against severe noise and corrupted fringe data regions, its performance is validated via numerical simulations. We also corroborate practical applicability by analyzing experimental test objects interferometry.

Language: Английский

Citations

2

Quantitative phase gradient metrology using diffraction phase microscopy and deep learning DOI
Allaparthi Venkata Satya Vithin, Rajshekhar Gannavarpu

Journal of the Optical Society of America A, Journal Year: 2023, Volume and Issue: 40(3), P. 611 - 611

Published: Jan. 31, 2023

In quantitative phase microscopy, measurement of the gradient is an important problem for biological cell morphological studies. this paper, we propose a method based on deep learning approach that capable direct estimation without requirement unwrapping and numerical differentiation operations. We show robustness proposed using simulations under severe noise conditions. Further, demonstrate method's utility imaging different cells diffraction microscopy setup.

Language: Английский

Citations

5

Central difference information filtering phase unwrapping algorithm based on deep learning DOI
Jiaying Li, Xianming Xie

Optics and Lasers in Engineering, Journal Year: 2023, Volume and Issue: 163, P. 107484 - 107484

Published: Jan. 23, 2023

Language: Английский

Citations

4