Effective Field Sources and Field-Source-Function for Stable Field Image Reconstruction

To solve the problems that the current stable field image reconstruction models still cause errors and the low reconstruction efficiency; an improved algorithm is proposed in this paper. Firstly, the interval of main texture direction at the defective pixel is calculated to determine the effective sources. Then, the differential approximation method is used to construct the field-source-function between the effective field sources and the defective pixel by using the low order Taylor expansion to complete the reconstruction. Compared with an improved algorithm based on CDD model, an improved algorithm based on Criminisi model and two algorithms based on stable field model, the peak signal-to-noise ratio of the proposed algorithm is improved by 1.5-2.6 dB on average; the accuracy of reconstruction is improved by 3.5%-7.9%. The experimental results on the selected images and CelebA-HQ face dataset show that the proposed algorithm achieves efficient and accurate reconstruction of different types of defect images.

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Publication Details

Journal
Journal of Computer-Aided Design & Computer Graphics
Published
2024-09-01
DOI
https://doi.org/10.3724/sp.j.1089.2024.19957
Primary Topic
Applied Advanced Technologies
Type
article
Field-Weighted Citation Impact
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article

Effective Field Sources and Field-Source-Function for Stable Field Image Reconstruction

Han Chen, Xueyi Ye, Lingyu Wang, Na Ying et al.
Journal of Computer-Aided Design & Computer Graphics
Applied Advanced Technologies
article

Effective Field Sources and Field-Source-Function for Stable Field Image Reconstruction

Han Chen, Xueyi Ye, Lingyu Wang, Na Ying, Maosheng Zeng
article en

Abstract

To solve the problems that the current stable field image reconstruction models still cause errors and the low reconstruction efficiency; an improved algorithm is proposed in this paper. Firstly, the interval of main texture direction at the defective pixel is calculated to determine the effective sources. Then, the differential approximation method is used to construct the field-source-function between the effective field sources and the defective pixel by using the low order Taylor expansion to complete the reconstruction. Compared with an improved algorithm based on CDD model, an improved algorithm based on Criminisi model and two algorithms based on stable field model, the peak signal-to-noise ratio of the proposed algorithm is improved by 1.5-2.6 dB on average; the accuracy of reconstruction is improved by 3.5%-7.9%. The experimental results on the selected images and CelebA-HQ face dataset show that the proposed algorithm achieves efficient and accurate reconstruction of different types of defect images.

Journal of Computer-Aided Design & Computer GraphicsVol. 36(9)
Openalex Percentile: Top 54%
Applied Advanced Technologies
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