贝叶斯多曝光图像融合技术用于稳健的高动态范围分色摄影。

IF 3.2 2区 物理与天体物理 Q2 OPTICS Optics express Pub Date : 2024-07-29 DOI:10.1364/OE.524284
Shantanu Kodgirwar, Lars Loetgering, Chang Liu, Aleena Joseph, Leona Licht, Daniel S Penagos Molina, Wilhelm Eschen, Jan Rothhardt, Michael Habeck
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引用次数: 0

摘要

探测器有限的动态范围会妨碍相干衍射成像(CDI)方案实现衍射极限分辨率。为了克服这一限制,一种直接的方法是通过多曝光图像融合(MEF)利用高动态范围(HDR)成像。这种方法包括在不同的曝光时间(从曝光不足到曝光过度)捕捉测量数据,然后将它们融合到一张 HDR 图像中。传统的分层摄影 MEF 技术通常是减去背景噪声,忽略饱和像素,然后合并采集结果。然而,这种方法在信噪比(SNR)较低的情况下并不适用。此外,光照强度的变化也会严重影响相位检索过程。为了解决这些问题,我们提出了一种基于修正泊松分布的贝叶斯 MEF 建模方法,该方法将背景和饱和度考虑在内。我们采用期望最大化(EM)算法来推断模型参数。合成数据和实验数据表明,我们的方法优于传统的 MEF 方法,能在具有挑战性的实验条件下提供出色的相位检索。这项工作强调了稳健的多曝光图像融合对于片纹摄影的重要性,尤其是在拍摄噪声占主导地位的弱散射标本成像中,或者在使用高信噪比的 HDR 检测器受到限制的情况下。此外,贝叶斯 MEF 方法的适用范围超出了 CDI,可用于任何需要 HDR 处理的成像方案。鉴于这种多功能性,我们以 Python 软件包的形式提供了我们算法的实现。
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Bayesian multi-exposure image fusion for robust high dynamic range ptychography.

The limited dynamic range of the detector can impede coherent diffractive imaging (CDI) schemes from achieving diffraction-limited resolution. To overcome this limitation, a straightforward approach is to utilize high dynamic range (HDR) imaging through multi-exposure image fusion (MEF). This method involves capturing measurements at different exposure times, spanning from under to overexposure and fusing them into a single HDR image. The conventional MEF technique in ptychography typically involves subtracting the background noise, ignoring the saturated pixels and then merging the acquisitions. However, this approach is inadequate under conditions of low signal-to-noise ratio (SNR). Additionally, variations in illumination intensity significantly affect the phase retrieval process. To address these issues, we propose a Bayesian MEF modeling approach based on a modified Poisson distribution that takes the background and saturation into account. The expectation-maximization (EM) algorithm is employed to infer the model parameters. As demonstrated with synthetic and experimental data, our approach outperforms the conventional MEF method, offering superior phase retrieval under challenging experimental conditions. This work underscores the significance of robust multi-exposure image fusion for ptychography, particularly in imaging shot-noise-dominated weakly scattering specimens or in cases where access to HDR detectors with high SNR is limited. Furthermore, the applicability of the Bayesian MEF approach extends beyond CDI to any imaging scheme that requires HDR treatment. Given this versatility, we provide the implementation of our algorithm as a Python package.

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来源期刊
Optics express
Optics express 物理-光学
CiteScore
6.60
自引率
15.80%
发文量
5182
审稿时长
2.1 months
期刊介绍: Optics Express is the all-electronic, open access journal for optics providing rapid publication for peer-reviewed articles that emphasize scientific and technology innovations in all aspects of optics and photonics.
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