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Developing variants of the Lucy-Richardson algorithm for coded aperture imaging: tutorial
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DOI：10.1364/JOSAA.573587.png)
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Abstract
En 中文
Deconvolution methods, originally developed for image deblurring, are foundational to coded aperture imaging (CAI) technologies. Among these, the Lucy-Richardson algorithm (LRA), first introduced over half a century ago, has seen renewed interest in CAI applications in recent years. Uniquely, LRA incorporates both convolution and cross-correlation operations, with the latter effectively functioning as an internal deconvolution step, offering a versatile platform for innovation. This tutorial presents the fundamentals of CAI alongside a detailed formulation of LRA. Strategies for enhancing LRA performance through modifications to the cross-correlation step are explored in depth. Both established variants, such as LRA with power-law transformation and limited support constraint, the Lucy-Richardson-Rosen algorithm, and novel extensions, including the interlooped LRA, are introduced. Future directions for designing LRA variants tailored to specific imaging scenarios are also discussed. Step-bystep MATLAB code examples are provided to guide researchers in developing custom LRA-based deconvolution approaches for advanced imaging applications. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
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CORRELATION HOLOGRAPHY-A
RESTORATION
ENSEMBLE
BEAM
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