Inpainting For fringe projection profilometry based on iterative regularization

Budianto, B.; Lun, Daniel Pak-Kong

doi:10.1109/icdsp.2014.6900748

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…The original IR problem described by Eq. ( 6) can be converted to: (7) Here, μ is the penalty parameter in the iterative process, k means the number of iterations. Solving Eq.…”

Section: Image Inpainting Based On Cnn Denoiser Priormentioning

confidence: 99%

“…Some scholars used the image processing technique to directly inpaint the fringe with saturation areas for avoiding time consuming and simplifying the measurement system. BUDIANTO and LUN [7] proposed an iterative regularization inpainting method based on a double tree complex wavelet transform. In reference [8], they demonstrated the importance of the election of an initial iteration fringe and calculated the initial fringe to guide inpainting of the saturated region according to the geometric morphology of the neighborhood stripes.…”

Section: Introductionmentioning

confidence: 99%

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Fringe pattern inpainting based on dual-exposure fused fringe guiding CNN denoiser prior

Guangze¹,

Chen²

2022

Optica Applicata

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The intensity of some pixels of the captured fringe will be saturated when fringe projection profilometry is used to measure objects with high reflectivity, which will significantly affect the reconstruction of the measured object. In this paper, we propose a fringe pattern inpainting method based on the convolutional neural network (CNN) denoiser prior guided by additional information from a fringe captured in short exposure time. First, a binary mask obtained by Otsu algorithm from the modulation information of the short exposure fringe is used to detect the high-saturation region in the normal exposure fringe. Then, the corrected gray-scales of the region of the short exposure fringe selected by the mask are inserted in the saturated region of the normal fringe to form an initial fringe for iteration. At last, fringe pattern inpainting is achieved by using a CNN denoiser prior. The correct phase can be reconstructed from the inpainted fringes. The computer simulation and experiments verify the effectiveness of the proposed method.

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“…The original IR problem described by Eq. ( 6) can be converted to: (7) Here, μ is the penalty parameter in the iterative process, k means the number of iterations. Solving Eq.…”

Section: Image Inpainting Based On Cnn Denoiser Priormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fringe pattern inpainting based on dual-exposure fused fringe guiding CNN denoiser prior

Guangze¹,

Chen²

2022

Optica Applicata

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“…where ( ) ℘ ⋅ is a smoothing operator in the wavelet domain similar to [49]; ĝ is the resulting smoothed fringe image; Ψ and T Ψ are the analysis and synthesis 2D-DTCWT operator, respectively. The 2D-DTCWT is chosen since the highlight detection is carried out within the 2D-DTCWT based FPP framework as described in Section II.…”

Section: Highlight Region Detectionmentioning

confidence: 99%

Inpainting for Fringe Projection Profilometry Based on Geometrically Guided Iterative Regularization

Budianto

Lun

2015

IEEE Trans. on Image Process.

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Conventional fringe projection profilometry methods often have difficulty in reconstructing the 3D model of objects when the fringe images have the so-called highlight regions due to strong illumination from nearby light sources. Within a highlight region, the fringe pattern is often overwhelmed by the strong reflected light. Thus, the 3D information of the object, which is originally embedded in the fringe pattern, can no longer be retrieved. In this paper, a novel inpainting algorithm is proposed to restore the fringe images in the presence of highlights. The proposed method first detects the highlight regions based on a Gaussian mixture model. Then, a geometric sketch of the missing fringes is made and used as the initial guess of an iterative regularization procedure for regenerating the missing fringes. The simulation and experimental results show that the proposed algorithm can accurately reconstruct the 3D model of objects even when their fringe images have large highlight regions. It significantly outperforms the traditional approaches in both quantitative and qualitative evaluations.

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“…Another important factor affecting the phase retrieval accuracy in FPP is pixel intensity saturation due to the high dynamic range reflectivity of the object surface, and global and direct illuminations. The fringe pattern inpainting methods [12][13][14][15] have been reported to address this issue. In the present work, a Fourier-Bessel series expansion based method is proposed for both fringe pattern denoising and inpainting.…”

Section: Introductionmentioning

confidence: 99%

Fourier-Bessel series expansion-based fringe pattern denoising and inpainting

Kulkarni

2024

Optics and Photonics for Advanced Dimensional Metrology III

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The fringe pattern denoising and inpainting operations are crucial for the accurate phase retrieval in the phasebased optical measurement techniques. In this work, a Fourier-Bessel series expansion (FBSE)-based algorithm is proposed for the fringe pattern denoising and inpainting. The FBSE provides a compact representation of wide-band fringe pattern for the effective fringe signal denoising. The Bessel functions are used for the accurate reconstruction of non-stationary fringe signal even in the events of loss of fringe data at some object points. A comparative study between the FBSE, windowed Fourier filtering and partial differential equation-based methods is presented for the fringe pattern denoising. The simulation and experimental results indicate the suitability of the proposed method in practical applications.

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Inpainting For fringe projection profilometry based on iterative regularization

Cited by 4 publications

References 16 publications

Fringe pattern inpainting based on dual-exposure fused fringe guiding CNN denoiser prior

Fringe pattern inpainting based on dual-exposure fused fringe guiding CNN denoiser prior

Inpainting for Fringe Projection Profilometry Based on Geometrically Guided Iterative Regularization

Fourier-Bessel series expansion-based fringe pattern denoising and inpainting

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