The Effect of Low-pass Pre-filtering on Subvoxel Registration Algorithms in Digital Volume Correlation: A revisited study

Zou, Xiang; Li, Kai; Pan, Bing

doi:10.2478/msr-2020-0025

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…Therefore, to elevate the DIC accuracy further, the improvement of imaging quality and denoising would be one of the key priorities. Some pioneer studies demonstrated that appropriate pre-processing methods could enhance the image quality and reduce the noise level, hence, effectively mitigating DIC computational errors to some extent [48][49] [50]. Inspired by the novel techniques in imaging processing [51][52] [53] and noisecancelling [54][55] [56], the performance of DIC algorithms on image samples with pre-processing and denoising techniques will be further investigated in our future studies.…”

Section: A Impact Of Noisementioning

confidence: 99%

Impact of Speckle Deformability on Digital Imaging Correlation

Wang,

Wu,

Zhu

et al. 2024

IEEE Access

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Digital Image Correlation (DIC) has been widely used as a non-contact deformation measurement technique. Nevertheless, its accuracy is greatly affected by the speckle pattern on the specimen. To systematically evaluate how speckle deformability affects the precision of DIC algorithms. In this study, a test dataset of 2D speckle patterns with various prescribed deformation fields was numerically generated, containing two categories of speckles, i.e., the deformable and the non-deformable (rigid) ones. This dataset was used to evaluate the performance of inverse compositional Gauss-Newton (ICGN)-based DIC algorithms with two types of shape function (first-order and second-order), in the different scenarios of the deformation field. The results showed that imaging noise had a significant influence on the DIC algorithm. The first-order shape function (ICGN-1) performed better when tracking the simple linear deformation field. While the second-order shape function (ICGN-2) was proved to perform better on nonlinear deformations. Moreover, the deformability of the speckle was found to have an obvious impact on the performance of the DIC algorithm. ICGN-2 could effectively reduce so-called speckle rigidity induced (SRI) error. Conclusively, ICGN-2 should be chosen as priority, because of its feasibility on non-linear deformation fields and speckle rigidity. While in the linear deformation scenarios, ICGN-1 was still a robust and efficient method.

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Section: A Impact Of Noisementioning

confidence: 99%

Impact of Speckle Deformability on Digital Imaging Correlation

Wang,

Wu,

Zhu

et al. 2024

IEEE Access

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“…If the signal is closer to the pixel space, the weighted value at the approximate point will be higher, so as to contribute better to the image filtering processing and retain the edge details. Combining the advantages of the wavelet threshold and the bilateral filtering, denoising the image will obtain a better processing effect of image edge detail [20]. As shown in Figure 3, the principle diagram of CT image filtering algorithm [21][22][23][24][25] of wavelet transformation and bilateral filtering is shown.…”

Section: Construction Of Ct Image Processing Model Based Onmentioning

confidence: 99%

Oral CT Image Processing Based on Oral CT Image Filtering Algorithm

Yang

Wang

Xiang

et al. 2022

Computational Intelligence and Neuroscience

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The application of computer vision technology in the medical field provides more accurate technical support for oral disease detection. The research proposes the CT image denoising algorithm based on wavelet and bilateral filtering. Through the study of the imaging principle of CT image and the CT image acquisition scene, the CT image data is filtered by wavelet and bilateral filtering algorithm, and the algorithm is proposed from the peak signal-to-noise ratio, structural similarity, and the effective detection of three-dimensional image construction of the image. The test results show that the proposed algorithm has excellent performance in the aspects of peak signal-to-noise ratio, structural similarity, and error of mean square. When the proportions of Gaussian noise are 10%, 20%, 30%, 40%, and 50%, the MSE error values of the proposed algorithm are 0.002, 0.004, 0.006, and 0.007, respectively, and the performance is the best in the comparison of multiple algorithms. The contents of the research provide an important theoretical reference for the treatment of clinical oral diseases.

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“…A long-standing solution is to smooth displacement data before numerical differentiation, for example, using mean or median filters [10] or low band pass filters [11,12]. Alternatively, local least-squares matching to a specified region is used to provide analytical derivatives for the fitting function, such as a lower-order polynomial [10].…”

Section: Introductionmentioning

confidence: 99%

A Robust Finite Element-based Filter for Digital Image and Volume Correlation Displacement Data

Becker

Marrow

2021

Exp Mech

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Background: Digital Image and Volume Correlation (DIC and DVC) are non-contact measurement techniques that are used during mechanical testing for quantitative mapping of full-field displacements. The relatively high noise floor of DIC and DVC, which is exasperated when differentiated to obtain strain fields, often requires some form of filtering. Techniques such as median filters or least-squares fitting perform poorly over high displacement gradients, such as the strain localisation near a crack tip, discontinuities across crack flanks or large pores.As such, filtering does not always effectively remove outliers in the displacement field.Objective: This work proposes a robust finite element-based filter that detects and replaces outliers in the displacement data using a finite element method-based approximation.Methods: A method is formulated for surface (2D and Stereo DIC) and volumetric (DVC) measurements. Its validity is demonstrated using analytical and experimental displacement data around cracks, obtained from surface and full volume measurements. Results:It is shown that the displacement data can be filtered in such a way that outliers are identified and replaced. Moreover, data can be smoothed whilst maintaining the nature of the underlying displacement field such as steep displacement gradients or discontinuities. Conclusions:The method can be used as a post-processing tool for DIC and DVC data and will support the use of the finite element method as an experimental-numerical technique.

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The Effect of Low-pass Pre-filtering on Subvoxel Registration Algorithms in Digital Volume Correlation: A revisited study

Cited by 8 publications

References 38 publications

Impact of Speckle Deformability on Digital Imaging Correlation

Impact of Speckle Deformability on Digital Imaging Correlation

Oral CT Image Processing Based on Oral CT Image Filtering Algorithm

A Robust Finite Element-based Filter for Digital Image and Volume Correlation Displacement Data

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