ORIGINAL RESEARCH article
Adv. Opt. Technol.
Sec. Optical Imaging
PSO-based imaging restoration method for diffraction imaging systems
Provisionally accepted
- 1Civil Aviation Flight University of China, Guanghan, China
- 2Yibin University, Yibin, China
- 3China West Normal University, Nanchong, China
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Membrane diffraction imaging is one of the most widely used imaging technologies today, which offers the advantages such as lightweight design, large aperture, foldability, and low cost. However, the system imaging quality degrades because of the multiple order diffraction generated by the diffractive elements in practical applications. To eliminate the effects of multiple diffraction orders from the diffractive elements and optimize imaging quality, the system images are post processed. Iterative optimization algorithms are commonly used for image post processing. Particle swarm optimization is a commonly used iterative optimization algorithm , which is often used to search for optimal solutions within the solution space. The particle swarm optimization algorithm has the features of few parameters, simple behavior, and fast iteration speed, which can rapidly and effectively optimize imaging. This paper optimizes the simulated imaging of a diffraction imaging system based on Fresnel zone plates by adopting the particle swarm optimization algorithm. Optimize the system image based on known point spread functions and the system image.System imaging is optimized under the premise of known point spread functions and system imaging. The iteration speed is enhanced, reducing the number of iterations by approximately 99.6% compared to the random parallel gradient descent algorithm. Simultaneously, contrast is improved by about 5.4%, while gradient optimization effectiveness increases by approximately 25.4% after optimization by the particle swarm algorithm. Finally, the derived restoration model was applied to other images, achieving overall improvements in all evaluation metrics.
Keywords: Fresnel zone plate, Imaging optimization, multiple orders diffraction, Particle swarm optimization algorithm, point spread function
Received: 23 Oct 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Li, WEN, Liu, Du, Li, Yang and Jiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhengcong Du
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