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Physics-driven self-supervised learning for parasitic-fringe suppression in lensless holography.
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Researchers developed a computer method that improves a technique for imaging tiny microplastic particles suspended in water, using lens-free equipment and light patterns. Tested on computer-simulated and real microplastic images, the method reduced unwanted light interference and sharpened the visibility of particle edges without needing paired training data.
Parasitic fringes degrade background stability and particle-boundary contrast in lensless holographic reconstruction. In this Letter, we propose a physics-driven self-supervised framework that decouples object-related, smooth-background, and directional parasitic-fringe components within an angular-spectrum forward model. An untrained network parameterizes the object-related complex field as an implicit deep image prior, while physics consistency and component-specific spatial constraints guide the decomposition without paired data. Experiments on simulated and experimental microplastic holograms show that the method suppresses structured coherent interference, stabilizes the background, and preserves particle-boundary contrast, providing a reconstruction route for label-free particle imaging in interference-prone aqueous measurements.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.