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

Shuzi Li

Journal of Function Spaces, Год журнала: 2022, Номер 2022, С. 1 - 11

Опубликована: Дек. 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 < ).

Язык: Английский

Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction DOI Creative Commons
Bo Chen,

Zhaoyi Li,

Yilin Zhou

и другие.

Applied Sciences, Год журнала: 2023, Номер 13(17), С. 9806 - 9806

Опубликована: Авг. 30, 2023

Addressing the issue of simultaneous reconstruction intensity and phase information in multiscale digital holography, an improved deep-learning model, Mimo-Net, is proposed. For holograms with uneven distribution useful information, local feature extraction performed to generate different scales, branch input training used realize learning, receptive fields obtained. The up-sampling path outputs simultaneously through dual channels. experimental results show that compared Y-Net, which a network capable reconstructing simultaneously, Mimo-Net can perform on three scales only one training, improving efficiency. peak signal-to-noise ratio structural similarity for are higher than those Y-Net reconstruction, performance.

Язык: Английский

Процитировано

0

Conditional generative modelling based fringe pattern normalization DOI

S. Narayan,

Viren S Ram,

Rajshekhar Gannavarpu

и другие.

Опубликована: Янв. 1, 2023

In this article, we propose a generative adversarial network based fringe pattern normalization method. We investigate the method's effectiveness under various noise levels by evaluating root mean square error (RMSE) and structural similarity index measure (SSIM).

Язык: Английский

Процитировано

0

Displacement derivative analysis using deep learning in digital holographic interferometry DOI
Allaparthi Venkata Satya Vithin, Jagadesh Ramaiah, Dhruvam Pandey

и другие.

Digital Holography and 3-D Imaging 2022, Год журнала: 2022, Номер unknown, С. W2A.7 - W2A.7

Опубликована: Янв. 1, 2022

In this article, we present deep learning approach to estimate displacement derivatives in digital holographic interferometry. The results show the capability of proposed method on noisy experimental fringes.

Язык: Английский

Процитировано

0

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

Shuzi Li

Journal of Function Spaces, Год журнала: 2022, Номер 2022, С. 1 - 11

Опубликована: Дек. 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 < ).

Язык: Английский

Процитировано

0