Image Reconstruction Based on Fractional Tikhonov Framework for Planar Array Capacitance Sensor

Sun, Yurong; Zhang, Yuyan; Wen, Yintang

doi:10.1109/tci.2022.3146810

Cited by 12 publications

(3 citation statements)

References 36 publications

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“…The Fractional Tikhonov regularization method is proposed in ref. 6 for image reconstruction to obtain the original image of the fused image [6] . Tikhonov regularization is used to solve Eq.…”

Section: Original Imagementioning

confidence: 99%

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Dynamic Detection Based on Planar Array Capacitance for Adhesive Defects of Thermal Protection Structure

Sun

Zhang

Wen

2023

J. Phys.: Conf. Ser.

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The defect detection of thermal protective structures is of great significance in ensuring the safe flight of aircraft. In this paper, a dynamic measurement method based on the planar capacitance sensing principle is proposed to solve the problem of detecting defects in the adhesive layer of large-area thermal protection structures. This paper analyses the principle of capacitive sensing and establishes a planar array capacitive dynamic detection system. The dielectric distribution of the structural components to be measured is obtained at different locations by moving the array of electrode sensors regularly. To obtain the dielectric distribution information of the complete structure, the dielectric distribution at adjacent locations is spliced and fused. To achieve the precise location of the overlapping areas in the image to be fused, an image alignment method based on coordinates is adopted. The experimental results show that our proposed method can detect defects in the adhesive layer of large-area thermal protection structures and achieve complete visualization of the adhesive layer.

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Section: Original Imagementioning

confidence: 99%

“…The reconstructed image obtained using fractional Tikhonov regularisation proposed in ref. 6 is used as the source image for image fusion and stitching [6] . To guarantee the accurate positioning of overlapping regions in the fused image, a coordinate-based image alignment method is employed.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Detection Based on Planar Array Capacitance for Adhesive Defects of Thermal Protection Structure

Sun

Zhang

Wen

2023

J. Phys.: Conf. Ser.

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“…(1.6) It is known that the fractional power has lower damping at small singular values and that fractional power has a greater effect on the filtering function [26] (also see [16]). While performing numerical calculations in practical applications, x δ α,β being the minimizer of J β α over an infinite dimensional space is not easy to compute.…”

Section: Introductionmentioning

confidence: 99%

Finite dimensional realization of a parameter choice strategy for fractional Tikhonov regularization method in Hilbert scales

Mekoth

George

Jidesh

2023

Hacettepe Journal of Mathematics and Statistics

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One of the most crucial parts of applying a regularization method to solve an ill-posed problem is choosing a regularization parameter to obtain an optimal order error estimate. In this paper, we consider the finite dimensional realization of the parameter choice strategy proposed in [C. Mekoth, S. George and P. Jidesh. Fractional Tikhonov regularization method in Hilbert scales. Appl. Math. Comput.(2021), 392: 125701, 26 DOI:10.1016/j.amc.2020.125701] for Fractional Tikhonov regularization method for linear ill-posed operator equations in the setting of Hilbert scales.

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Hybrid regularization inspired by total variation and deep denoiser prior for image restoration

Liang,

Zhang,

Wei

et al. 2024

Complex Intell. Syst.

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Image restoration is a fundamental problem in computer vision, with the goal of restoring high-quality images from degraded low-quality observation images. However, the ill-posedness of restoration problem often leads to artifacts in the results. It inspires us to study the combination of prior, effectively restrict the solution space and improving the quality of the restored image. In this paper, a novel hybrid regularization method for image restoration is proposed, which adopts both internal and external image priors into a unified framework. Specifically, a new hybrid regularization model is designed. The TV model and a deep image denoiser are inserted into the restoration model using the plug-and-play framework, protecting image details while implicitly using external information for deep denoising. Moreover, in order to make the proposed hybrid regularization operable, an adaptive parameter method is proposed to adaptively balance the TV model and learned model in iteration. Experiments show that the proposed method surpasses the performance of existing image restoration techniques.

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Image Reconstruction Based on Fractional Tikhonov Framework for Planar Array Capacitance Sensor

Cited by 12 publications

References 36 publications

Dynamic Detection Based on Planar Array Capacitance for Adhesive Defects of Thermal Protection Structure

Dynamic Detection Based on Planar Array Capacitance for Adhesive Defects of Thermal Protection Structure

Finite dimensional realization of a parameter choice strategy for fractional Tikhonov regularization method in Hilbert scales

Hybrid regularization inspired by total variation and deep denoiser prior for image restoration

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