Author:
Seong Baekcheon,Kim Woovin,Kim Younghun,Hyun Kyung-A,Jung Hyo-Il,Lee Jong-Seok,Yoo Jeonghoon,Joo Chulmin
Abstract
AbstractSeveral image-based biomedical diagnoses require high-resolution imaging capabilities at large spatial scales. However, conventional microscopes exhibit an inherent trade-off between depth-of-field (DoF) and spatial resolution, and thus require objects to be refocused at each lateral location, which is time consuming. Here, we present a computational imaging platform, termed E2E-BPF microscope, which enables large-area, high-resolution imaging of large-scale objects without serial refocusing. This method involves a physics-incorporated, deep-learned design of binary phase filter (BPF) and jointly optimized deconvolution neural network, which altogether produces high-resolution, high-contrast images over extended depth ranges. We demonstrate the method through numerical simulations and experiments with fluorescently labeled beads, cells and tissue section, and present high-resolution imaging capability over a 15.5-fold larger DoF than the conventional microscope. Our method provides highly effective and scalable strategy for DoF-extended optical imaging system, and is expected to find numerous applications in rapid image-based diagnosis, optical vision, and metrology.
Publisher
Springer Science and Business Media LLC
Subject
Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Cited by
1 articles.
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