Nature MethodsBioinformaticsPeer-reviewed journal article
Chromatix: a differentiable, GPU-accelerated wave-optics library
Diptodip DebiD, Gert‐Jan BothiD, Eric BezzamiD, Amit KohliiD, Siqi YangiD, Amey ChawareiD, Cedric P. AllieriD, Changjia CaiiD, Geneva AnderbergiD, M. Hossein EybposhiD, Magdalena C. SchneideriD, Rainer HeintzmanniD, Fabrizio A. Rivera-SancheziD, Corey Simmerer, Guanghan MengiD, Jovan Tormes-VaqueranoiD, SeungYun HaniD, Sibi Chakravarthy ShanmugaveliD, Teja MaruvadaiD, Xi Dang YangiD, Yewon KimiD, Benedict DiederichiD, Chulmin JooiD, Laura WalleriD, Nicholas J. DurriD, , Srinivas C. TuragaiD
Abstract
Modern microscopy methods incorporate computational modeling as an integral part of the imaging process, either to solve inverse problems or optimize the optical system design itself. These methods often depend on differentiable optics simulations, yet no standardized framework exists--forcing computational optics researchers to repeatedly and independently implement simulations with limited reusability and performance. These common problems limit the potential impact of computational optics as a field. Here we present Chromatix: an open-source, GPU-accelerated, differentiable wave optics simulation library. Chromatix builds on JAX to democratize fast, parallelized simulation of diverse optical systems and expand the design space in computational optics. Chromatix standardizes a growing collection of optical elements and propagation methods allowing a broad range of applications, which we demonstrate here for snapshot microscopy, holography, and phase retrieval. We demonstrate speed improvements of 2-6x on a single GPU and up to 22x on 8 GPUs.
- research focus: software / tool
- research focus: benchmarking
- approach: deep learning
- approach: statistical model
- Citations
- 2
- FWCI
- 2.58
- Field percentile
- top 19%
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