Physics Maths Engineering
E2E-BPF microscope: Extended depth-of-field microscopy using learning-based implementation of binary phase filter and image deconvolution
Baekcheon Seong,
Woovin Kim,
Younghun Kim,
Jong-Seok Lee,
Jeonghoon Yoo,
Chulim Joo
Peer Reviewed
Related Subjects
Abstract
Abstract Several 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.
Key Questions
What is the E2E-BPF microscope?
The E2E-BPF microscope is a computational imaging platform designed to overcome the trade-off between depth-of-field (DoF) and spatial resolution in conventional microscopes. It enables high-resolution imaging over large areas without the need for time-consuming refocusing.
Why is high-resolution imaging at large scales important?
High-resolution imaging at large scales is crucial for biomedical diagnoses, such as analyzing tissue samples or cells. It allows researchers and clinicians to observe fine details over wide areas, improving accuracy and efficiency in diagnostics.
What is the main limitation of conventional microscopes?
Conventional microscopes struggle with a trade-off between depth-of-field (DoF) and spatial resolution. To image large objects, they require serial refocusing at each lateral location, which is slow and impractical for large-scale imaging.
How does the E2E-BPF microscope solve this problem?
The E2E-BPF microscope uses a physics-incorporated, deep-learned binary phase filter (BPF) and a jointly optimized deconvolution neural network. This combination extends the depth-of-field by 15.5 times compared to conventional microscopes, enabling high-resolution imaging without refocusing.
What is a binary phase filter (BPF)?
A binary phase filter (BPF) is an optical component that modifies the phase of light passing through it. In the E2E-BPF microscope, the BPF is designed using deep learning to optimize imaging performance over extended depth ranges.
How does deep learning improve the E2E-BPF microscope?
Deep learning is used to design the BPF and optimize the deconvolution neural network. This ensures high-resolution, high-contrast images over large areas, even at extended depths, by learning and compensating for optical aberrations.
What are the key benefits of the E2E-BPF microscope?
The E2E-BPF microscope:
- Eliminates the need for serial refocusing, saving time.
- Provides high-resolution imaging over a 15.5x larger depth-of-field.
- Produces high-contrast images, even for large-scale objects.
- Is scalable and adaptable for various biomedical and industrial applications.
How was the E2E-BPF microscope tested?
The microscope was tested through numerical simulations and experiments with fluorescently labeled beads, cells, and tissue sections. Results demonstrated its ability to maintain high resolution and contrast over extended depths.
What are the practical applications of this technology?
The E2E-BPF microscope is ideal for:
- Rapid image-based biomedical diagnoses.
- High-throughput screening of cells and tissues.
- Industrial metrology and quality control.
- Optical vision systems in robotics and automation.
How does this compare to traditional imaging methods?
Traditional methods require constant refocusing and struggle with depth-of-field limitations. The E2E-BPF microscope overcomes these challenges, offering faster, more efficient, and higher-quality imaging over large areas.
What are the future directions for this technology?
Future research could focus on:
- Further optimizing the BPF and neural network for specific applications.
- Expanding the technology to 3D imaging and real-time diagnostics.
- Integrating the microscope with AI for automated analysis and decision-making.
