Fringe patterns' denoising in electronic speckle pattern interferometry (ESPI) is an important step in phase extraction. In this study, we propose a new denoising method for ESPI fringe patterns based on a convolutional neural network (CNN). The proposed model which combines the attention mechanism and CNN is defined as attention-denoising CNN. In this model, owing to the attention mechanism, more attention will be paid to fringe information, and better filtering results will be achieved. The experimental results show that our proposed method can obtain excellent results, especially with high and large variation density ESPI fringe patterns.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/JOSAA.471359 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
UN-PUNet for phase unwrapping from a single uneven and noisy ESPI phase pattern.
J Opt Soc Am A Opt Image Sci Vis
October 2023
The wrapped phase patterns of objects with varying materials exhibit uneven gray values. Phase unwrapping is a tricky problem from a single wrapped phase pattern in electronic speckle pattern interferometry (ESPI) due to the gray unevenness and noise. In this paper, we propose a convolutional neural network (CNN) model named UN-PUNet for phase unwrapping from a single wrapped phase pattern with uneven grayscale and noise.
View Article and Find Full Text PDFIn view of the limitation of the traditional method to recover the phase of the single fringe pattern, we propose a digital phase-shift method based on distance mapping for phase recovery of an electronic speckle pattern interferometry fringe pattern. First, the direction of each pixel point and the centerline of the dark fringe are extracted. Secondly, the normal curve of the fringe is calculated according to the fringe orientation to obtain the fringe moving direction.
View Article and Find Full Text PDFThe fringe skeleton extraction method may be the most straightforward method for electronic speckle pattern interferometry (ESPI) phase extraction. Due to ESPI fringe patterns having the characteristics of high noise, low contrast, and different fringe shapes, it is very difficult to extract skeletons from ESPI fringe patterns with high accuracy. To deal with this problem, we propose a skeleton extraction method based on deep learning, called channel transformer U-Net, for directly extracting skeletons from noisy ESPI fringe patterns.
View Article and Find Full Text PDFApplication of attention-DnCNN for ESPI fringe patterns denoising.
J Opt Soc Am A Opt Image Sci Vis
November 2022
Fringe patterns' denoising in electronic speckle pattern interferometry (ESPI) is an important step in phase extraction. In this study, we propose a new denoising method for ESPI fringe patterns based on a convolutional neural network (CNN). The proposed model which combines the attention mechanism and CNN is defined as attention-denoising CNN.
View Article and Find Full Text PDFSimultaneous speckle reduction and contrast enhancement for electronic speckle pattern interferometry (ESPI) fringe patterns is a challenging task. In this paper, we propose a joint enhancement and denoising method based on the oriented variational Retinex model for ESPI fringe patterns with low contrast or uneven illumination. In our model, we use the structure prior to constrain the illumination and introduce a fractional-order differential to constrain the reflectance for enhancement, then use the second-order partial derivative of the reflectance as the denoising term to reduce noise.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!