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

BM3D adaptive TV filtering-based convolutional neural network for ESPI image denoising

Image denoising is a fundamental part of image processing. The real electronic speckle pattern interferometry (ESPI) contains a large amount of speckle noise, which affects the image quality and adversely affects subsequent studies. In this paper, a method based on an improved denoising convolutional neural network (CNN) has been proposed, with the goal of reducing noise while maintaining accurate information. The block matching 3D-based adaptive TV denoising CNN can protect the valid information while preventing the information of the original image itself from being corrupted. A two-channel model is used to improve the noise reduction effect of real images. The proposed method is compared with the conventional denoising algorithms and the deep-learning denoising algorithms. Experimental results show that the proposed method can maintain accuracy, integrity, and stability while preserving the details, texture, and edge information of the stripe pattern.

Fringe Phase-Shifting Field Based Fuzzy Quotient Space-Oriented Partial Differential Equations Filtering Method for Gaussian Noise-Induced Phase Error

Traditional filtering methods only focused on improving the peak signal-to-noise ratio of the single fringe pattern, which ignore the filtering effect on phase extraction. Fringe phase-shifting field based fuzzy quotient space-oriented partial differential equations filtering method is proposed to reduce the phase error caused by Gaussian noise while filtering. First, the phase error distribution that is caused by Gaussian noise is analyzed. Furthermore, by introducing the fringe phase-shifting field and the theory of fuzzy quotient space, the modified filtering direction can be adaptively obtained, which transforms the traditional single image filtering into multi-image filtering. Finally, the improved fourth-order oriented partial differential equations with fidelity item filtering method is established. Experiments demonstrated that the proposed method achieves a higher signal-to-noise ratio and lower phase error caused by noise, while also retaining more edge details.