Image inpainting by Kriging interpolation technique for mask removal

Sapkal, Monali S.; Kadbe, Premanand K.; Deokate, Balasaheb

doi:10.1109/iceeot.2016.7755260

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…The segmentation method using gray threshold and manual labeling is shown in It is crucial to generate a higher-quality mask for image inpainting in the reflection region and improve position accuracy [8,9]. The quality of the mask generated from the reflective area depended on the environment in which the image was generated.…”

Section: Circular Hole Recognitionmentioning

confidence: 99%

Surface-Framework structure: A neural network structure for weakening gridding effect in PCB mark-point semantic segmentation

et al. 2023

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Image transfer plays a significant role in the manufacture of PCB; it affects the production speed and quality of the manufacturing process. This study proposes a surface-framework structure, which divides the network into two parts: surface and framework. The surface part does not include subsampling to extract the detailed features of the image, thereby improving the segmentation effect when the computing power requirement is not large. Meanwhile, a semantic segmentation method based on Unet and surface-framework structure, called pure efficient Unet (PE Unet), is proposed. A comparative experiment is conducted on our mark-point dataset (MPRS). The proposed model achieved good results in various metrics. The proposed network’s IoU attained 84.74%, which is 3.15% higher than Unet. The GFLOPs is 34.0 which shows that the network model balances performance and speed. Furthermore, comparative experiments on MPRS, CHASE_DB1, TCGA-LGG datasets for Surface-Framework structure are introduced, the IoU promotion clipped means on these datasets are 2.38%, 4.35% and 0.78% respectively. The Surface-Framework structure can weaken the gridding effect and improve the performance of semantic segmentation network.

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Section: Circular Hole Recognitionmentioning

confidence: 99%

Surface-Framework structure: A neural network structure for weakening gridding effect in PCB mark-point semantic segmentation

et al. 2023

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“…Despite the full-rank assumption, it involves a relatively low computational cost because the core tensor is sparse with non-zero entries determined by the available pixels. Motivated by several works [48][49][50][51] on the use of various interpolation methods for solving image completion problems, other recent works [52][53][54][55][56] on image processing aspects, and the concept of tensor product spline surfaces [57], we show the relationship of the Tucker decomposition with factorizable radial basis function (RBF) interpolation and use it to compute the factor matrices. RBF interpolation is a mesh-free method, which is very profitable for recovering irregularly distributed missing pixels, but it may incorrectly approximate linear structure.…”

Section: Introductionmentioning

confidence: 99%

Image Completion with Hybrid Interpolation in Tensor Representation

Zdunek¹,

Sadowski²

2020

Applied Sciences

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The issue of image completion has been developed considerably over the last two decades, and many computational strategies have been proposed to fill-in missing regions in an incomplete image. When the incomplete image contains many small-sized irregular missing areas, a good alternative seems to be the matrix or tensor decomposition algorithms that yield low-rank approximations. However, this approach uses heuristic rank adaptation techniques, especially for images with many details. To tackle the obstacles of low-rank completion methods, we propose to model the incomplete images with overlapping blocks of Tucker decomposition representations where the factor matrices are determined by a hybrid version of the Gaussian radial basis function and polynomial interpolation. The experiments, carried out for various image completion and resolution up-scaling problems, demonstrate that our approach considerably outperforms the baseline and state-of-the-art low-rank completion methods.

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“…Currently, there are many image inpainting methods, including methods based on partial differential equation (PDE) [1][2][3][4], methods based on texture synthesis [5,6], methods based on sparse representation [7][8][9][10], and other methods [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Pathak et al [14] presented an unsupervised visual feature learning algorithm driven by context-based pixel prediction, and by the context encoder, the appearance and the semantics of visual structures were captured. In [15], an image inpainting algorithm based on Kriging interpolation technique was proposed, where the Kriging interpolation technique automatically could fill the damaged region and scratched regions.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Image Inpainting Method Based on Continued Fractions Interpolation

Xing

Xia

et al. 2018

Discrete Dynamics in Nature and Society

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In view of the drawback of most image inpainting algorithms by which texture was not prominent, an adaptive inpainting algorithm based on continued fractions was proposed in this paper. In order to restore every damaged point, the information of known pixel points around the damaged point was used to interpolate the intensity of the damaged point. The proposed method included two steps; firstly, Thiele's rational interpolation combined with the mask image was used to interpolate adaptively the intensities of damaged points to get an initial repaired image, and then Newton-Thiele's rational interpolation was used to refine the initial repaired image to get a final result. In order to show the superiority of the proposed algorithm, plenty of experiments were tested on damaged images. Subjective evaluation and objective evaluation were used to evaluate the quality of repaired images, and the objective evaluation was comparison of Peak Signal to Noise Ratios (PSNRs). The experimental results showed that the proposed algorithm had better visual effect and higher Peak Signal to Noise Ratio compared with the state-of-the-art methods.

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Image inpainting by Kriging interpolation technique for mask removal

Cited by 6 publications

References 4 publications

Surface-Framework structure: A neural network structure for weakening gridding effect in PCB mark-point semantic segmentation

Surface-Framework structure: A neural network structure for weakening gridding effect in PCB mark-point semantic segmentation

Image Completion with Hybrid Interpolation in Tensor Representation

An Adaptive Image Inpainting Method Based on Continued Fractions Interpolation

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