Fuzzy c-means clustering based segmentation and the filtering method for discontinuous ESPI fringe patterns

Area of Science:

• Optical Metrology
• Image Processing
• Interferometry

Background:

• Electronic Speckle Pattern Interferometry (ESPI) fringe patterns often suffer from noise and discontinuities, complicating analysis.
• Existing methods struggle to effectively filter these patterns while preserving crucial fringe information.

Purpose of the Study:

• To develop a robust method for segmenting and denoising discontinuous ESPI fringe patterns.
• To preserve fringe shapes and discontinuity information during the filtering process.

Main Methods:

• Fuzzy c-means (FCM) clustering applied to fringe orientation for segmentation.
• Generation of a discontinuous region mask.
• An adaptive shape-preserving oriented partial differential equation (OPDE) model incorporating the mask for denoising.

Main Results:

• Successful segmentation of discontinuous ESPI fringe patterns into continuous segments.
• Effective noise elimination using the adaptive OPDE model.
• Preservation of fringe shapes and discontinuity features in the processed patterns.

Conclusions:

• The proposed FCM-based segmentation and adaptive OPDE denoising method effectively addresses challenges in processing discontinuous ESPI fringe patterns.
• The technique accurately identifies discontinuities and preserves fringe integrity, outperforming existing methods.