Image definition assessment based on Tchebichef moments for micro-imaging

Area of Science:

• Optics and Photonics
• Image Processing
• Metrology

Background:

• Accurate image definition assessment (IDA) is crucial for micro-imaging systems.
• Existing methods can be influenced by original image content and lack sensitivity to defocus.
• Developing robust and sensitive IDA techniques is an ongoing challenge.

Purpose of the Study:

• To propose a novel Tchebichef moment (TM)-based image definition assessment (IDA) method.
• To enhance sensitivity to defocus amount in micro-imaging systems.
• To improve the anti-noise performance of IDA methods.

Main Methods:

• Utilized difference in logarithmic spectra (DLS) for image analysis.
• Extracted the point spread function (PSF) from DLS to uniquely characterize the IDA function.
• Employed low-order Tchebichef moments (TMs) to describe DLS with geometric feature variations.
• Amplified the PSF spot radius relative to the defocus amount to boost sensitivity.

Main Results:

• The proposed TM-based IDA method demonstrated superior performance compared to existing techniques.
• The method showed enhanced sensitivity to defocus amount in micro-imaging.
• The use of low-order TMs improved the anti-noise capabilities of the IDA function.
• Simulations and experiments validated the effectiveness of the proposed approach.

Conclusions:

• The Tchebichef moment-based IDA method using DLS is effective for micro-imaging systems.
• The technique offers improved sensitivity and robustness against noise.
• This method provides a unique and reliable approach for image definition assessment.