@article {69902,
	title = {Chromatix: A differentiable, GPU-accelerated wave-optics library},
	journal = {bioRxiv},
	year = {2025},
	month = {2025 May 2},
	abstract = {<p>Modern microscopy methods incorporate computational modeling of optical systems as an integral part of the imaging process, either to solve inverse problems or enable optimization of the optical system design. These methods often depend on differentiable simulations of optical systems, yet no standardized framework exists{\textemdash}forcing computational optics researchers to repeatedly and independently implement simulations that are prone to errors, difficult to reuse in other applications, and often computationally suboptimal. These common problems limit the potential impact of computational optics as a field. We present Chromatix: an open-source, GPU-accelerated differentiable wave optics library. Chromatix builds on JAX to enable fast simulation of diverse optical systems and inverse problem solving, scaling these simulations from single-CPU laptops to multi-GPU servers. The library implements various optical elements (e.g., lenses, polarizers and spatial light modulators) and multiple light propagation models (e.g., Fresnel approximation, angular spectrum and off-axis propagation) that can be flexibly combined to model various computational optics applications such as snapshot microscopy, holography, and phase retrieval of multiple scattering samples. These simulations can be automatically parallelized to scale across multiple GPUs with a single-line change to the modeling code, enabling simulation and optimization of previously impractical optical system designs. We demonstrate Chromatix{\textquoteright}s capacity to substantially accelerate optics simulation and optimization on existing methods in computational optics, speeding up optical simulation and optimization from 2-6{\texttimes} on a single GPU to up to 22{\texttimes} on 8 GPUs (depending on the particular system being modeled) compared to the original implementations. Chromatix establishes a standard for wave optics simulations, democratizing access to and expanding the design space of computational optics.i</p>
},
	doi = {10.1101/2025.04.29.651152},
	url = {https://www.biorxiv.org/content/early/2025/05/02/2025.04.29.651152},
	author = {Deb, Diptodip and Both, Gert-Jan and Bezzam, Eric and Kohli, Amit and Yang, Siqi and Chaware, Amey and Allier, C{\'e}dric and Cai, Changjia and Anderberg, Geneva and Eybposh, M. Hossein and Schneider, Magdalena C. and Heintzmann, Rainer and Rivera-Sanchez, Fabrizio A. and Simmerer, Corey and Meng, Guanghan and Tormes-Vaquerano, Jovan and Han, SeungYun and Shanmugavel, Sibi Chakravarthy and Maruvada, Teja and Yang, Xi and Kim, Yewon and Diederich, Benedict and Joo, Chulmin and Waller, Laura and Durr, Nicholas J. and P{\'e}gard, Nicolas C. and La Rivi{\`e}re, Patrick J. and Horstmeyer, Roarke and Chowdhury, Shwetadwip and Turaga, Srinivas C.}
}
@article {70176,
	title = {Revisiting PSF models: Unifying framework and high-performance implementation.},
	journal = {J Microsc},
	year = {2025},
	month = {2025 Nov 04},
	abstract = {<p>Localisation microscopy often relies on detailed models of point-spread functions. For applications such as deconvolution or PSF engineering, accurate models for light propagation in imaging systems with a high numerical aperture are required. Different models have been proposed based on 2D Fourier transforms or 1D Bessel integrals. The most precise ones combine a vectorial description of the electric field and accurate aberration models. However, it may be unclear which model to choose as there is no comprehensive comparison between the Fourier and Bessel approaches yet. Moreover, many existing libraries are written in Java (e.g., our previous PSF generator software) or MATLAB, which hinders their integration into deep learning algorithms. In this work, we start from the original Richards-Wolf integral and revisit both approaches in a systematic way. We present a unifying framework in which we prove the equivalence between the Fourier and Bessel strategies and detail a variety of correction factors applicable to both of them. Then, we provide a high-performance implementation of our theoretical framework in the form of an open-source library that is built on top of PyTorch, a popular library for deep learning. It enables us to benchmark the accuracy and computational speed of different models and allows for an in-depth comparison of the existing models for the first time. We show that the Bessel strategy is optimal for axisymmetric beams, while the Fourier approach can be applied to more general scenarios. Our work enables the efficient computation of a point-spread function on CPU or GPU, which can then be included in simulation and optimisation\&nbsp;pipelines.</p>
},
	issn = {1365-2818},
	doi = {10.1111/jmi.70045},
	url = {https://doi.org/10.1111/jmi.70045},
	author = {Liu, Yan and Stergiopoulou, Vasiliki and Chuah, Jonathan and Bezzam, Eric and Both, Gert-Jan and Unser, Michael and Sage, Daniel and Dong, Jonathan}
}
