The generation of a specific laser beam profile on the work surface is key to various laser beam shaping tasks, relying heavily on diffractive optical elements (DOEs). Most beam-shaping DOEs are designed using iterative Fourier transform algorithms (IFTAs), which generally have slow convergence and prone to stagnate at local minima. Moreover, the microreliefs generated by IFTAs tend to be irregular, complicating manufacturing and causing uncontrolled scattering of light. We propose a differentiable DOE design method that applies a phase-smoothness constraint using multi-level B-splines. A multi-scale gradient-descent optimization strategy, naturally linked with the multi-level B-splines, is employed to robustly determine the optimized phase distribution that is fully continuous. This, in turn, can lead to more regular DOE microreliefs, which can simplify the fabrication process and be less sensitive to changes in wavelength and working distance. Furthermore, our method can also design a fully continuous freeform lens, distinguished from most freeform lens design approaches by its foundation in physical optics rather than geometrical optics. Simulation and experimental results of several design tasks demonstrate the effectiveness of the proposed method.

Download full-text PDF

Source
http://dx.doi.org/10.1364/OE.533298DOI Listing

Publication Analysis

Top Keywords

multi-level b-splines
12
diffractive optical
8
optical elements
8
beam shaping
8
phase distribution
8
laser beam
8
fully continuous
8
freeform lens
8
differentiable design
4
design freeform
4

Similar Publications

The generation of a specific laser beam profile on the work surface is key to various laser beam shaping tasks, relying heavily on diffractive optical elements (DOEs). Most beam-shaping DOEs are designed using iterative Fourier transform algorithms (IFTAs), which generally have slow convergence and prone to stagnate at local minima. Moreover, the microreliefs generated by IFTAs tend to be irregular, complicating manufacturing and causing uncontrolled scattering of light.

View Article and Find Full Text PDF

Introduction: Uncontrolled family factors may bias the estimation of the association between maternal smoking during pregnancy and offspring body mass index (BMI). The objective was to assess if there is an association between maternal smoking during pregnancy and offspring BMI z-score independent of factors in the siblings' shared environment and if such association is linear.

Methods: We performed an individual patient data meta-analysis using five studies providing sibling data (45,299 children from 14,231 families).

View Article and Find Full Text PDF

Myocardial motion estimation of tagged cardiac magnetic resonance images using tag motion constraints and multi-level b-splines interpolation.

Magn Reson Imaging

May 2016

Center for Machine Vision and Security Research, Kennesaw State University, Marietta, GA, USA; Sino-US Intelligent Information Processing Joint Laboratory, Anyang Normal University, Anyang, Henan, China. Electronic address:

Myocardial motion estimation of tagged cardiac magnetic resonance (TCMR) images is of great significance in clinical diagnosis and the treatment of heart disease. Currently, the harmonic phase analysis method (HARP) and the local sine-wave modeling method (SinMod) have been proven as two state-of-the-art motion estimation methods for TCMR images, since they can directly obtain the inter-frame motion displacement vector field (MDVF) with high accuracy and fast speed. By comparison, SinMod has better performance over HARP in terms of displacement detection, noise and artifacts reduction.

View Article and Find Full Text PDF

Learning best features and deformation statistics for hierarchical registration of MR brain images.

Inf Process Med Imaging

August 2007

Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.

A fully learning-based framework has been presented for deformable registration of MR brain images. In this framework, the entire brain is first adaptively partitioned into a number of brain regions, and then the best features are learned for each of these brain regions. In order to obtain overall better performance for both of these two steps, they are integrated into a single framework and solved together by iteratively performing region partition and learning the best features for each partitioned region.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!