Improving spatial accuracy and signal-to-noise ratio through Babinet-principle-based coherent amplitude modulation...

Area of Science:

• Coherent imaging
• Phase retrieval
• Computational optics

Background:

• Accurate illumination characterization is crucial for high-resolution imaging.
• Conventional methods often face limitations in noise suppression and detail recovery.
• Existing techniques may require hardware modifications for performance enhancement.

Purpose of the Study:

• To propose a novel Babinet-principle-based coherent amplitude modulation imaging (BP-CAMI) method.
• To achieve accurate illumination characterization and enhance phase retrieval.
• To improve signal-to-noise ratio and recover high-frequency details in imaging.

Main Methods:

• Utilizing a random amplitude plate and a complementary constraint derived from the Babinet principle.
• Establishing a dual-constraint framework for algorithmic redundancy.
• Implementing phase retrieval algorithms to suppress noise and artifacts.

Main Results:

• Demonstrated substantial improvements in signal-to-noise ratio and spatial accuracy through simulations and experiments.
• BP-CAMI recovered richer high-frequency details compared to the ptychographic iterative engine.
• Achieved quantitative gains of 2.85 dB PSNR and 7.81 μm resolvable linewidth.

Conclusions:

• The proposed BP-CAMI method offers an effective, algorithm-driven solution for illumination characterization.
• The dual-constraint framework significantly enhances phase retrieval accuracy and image quality.
• BP-CAMI provides a practical approach to improve performance in coded-imaging systems without hardware modifications.