Screen Window Propagating for Image Inpainting

Liu, Huaming; Bi, Xuehui; Lu, Guanming; Wang, Weilan; Yan, Jun; Zhang, Zhengyan

doi:10.1109/access.2018.2876161

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…Higher the priority of the pixel region filling is faster. Huaming LIU [12] have proposed a method of screen window propagation for image inpainting. Where the higher layer leads the bottom layer inpainting for various resolution reduction.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Technique for Exemplar Based Image Inpainting Method

Patil¹,

Malemath²,

Muddapur³

2023

Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVA

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Image Inpainting expertise in reconstructing mislaid image parts. It is the technique for filling an unknown area or scratched area of an Image developed on the nearby information present in the image that is not recognizable by ordinary viewer. In proposed method two Inpainting algorithms, the traditional exemplarbased Image Inpainting and structure tensor inpainting technique has been used. The traditional Criminisi's Image Inpainting method is simple and fast but veracity of the image is weakened and the output image results in blurriness's. To resolve this issue structure tensor Inpainting method has been introduced in the proposed method. The structure tensor algorithm gives more accuracy of the image by using Image structure tensor information. The experimental results are obtained by comparing the Criminisi's method and the proposed method by PSNR, SSIM, MSE and SNR. Structure tensor method significantly improves Inpainting quality compared with traditional Criminisi's method.

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Section: Literature Reviewmentioning

confidence: 99%

Enhanced Technique for Exemplar Based Image Inpainting Method

Patil¹,

Malemath²,

Muddapur³

2023

Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVA

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“…These methods are commonly used to remove object from images. Liu et al [23] used structural similarity index measure (SSIM), obtained the best candidate patch by four cases of rotation and inversion, so as to find the most similar exemplar patch. Isogawa et al [21] noted the important effect of the mask on the restoration results, proposed a mask optimization method and used it to automatically obtain good results.…”

Section: Related Workmentioning

confidence: 99%

An image inpainting method for object removal based on difference degree constraint

Zhang

Chang

2020

Multimed Tools Appl

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In the inpainting method for object removal, SSD (Sum of Squared Differences) is commonly used to measure the degree of similarity between the exemplar patch and the target patch, which has a very important impact on the restoration results. Although the matching rule is relatively simple, it is likely to lead to the occurrence of mismatch error. Even worse, the error may be accumulated along with the process continues. Finally some unexpected objects may be introduced into the target region, making the result unable to meet the requirements of visual consistency. In view of these problems, we propose an inpainting method for object removal based on difference degree constraint. Firstly, we define the MSD (Mean of Squared Differences) and use it to measure the degree of differences between corresponding pixels at known positions in the target patch and the exemplar patch. Secondly, we define the SMD (Square of Mean Differences) and use it to measure the degree of differences between the pixels at known positions in the target patch and the pixels at unknown positions in the exemplar patch. Thirdly, based on MSD and SMD, we define a new matching rule and use it to find the most similar exemplar patch in the source region. Finally, we use the exemplar patch to restore the target patch. Experimental results show that the proposed method can effectively prevent the occurrence of mismatch error and improve the restoration effect.

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“…Currently, image inpainting methods are divided into three main types [1][2][3][4][5][6]: the variational partial di erentialbased method, texture synthesis-based method, and deep learning-based method. In the rst type, the heat ow partial di erential equation in physics is used to di use the information around to the inside of the broken area in the image to repair images.…”

Section: Introductionmentioning

confidence: 99%

A Criminisi-DnCNN Model-Based Image Inpainting Method

Zhu

Wang

2022

Mathematical Problems in Engineering

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Existing image inpainting methods achieve unideal results in dealing with centralized inpainting areas. For this reason, in this study, a Criminisi-DnCNN model-based image inpainting method is proposed. Inspired by the manual inpainting technology, the pointwise mutual information (PMI) algorithm was adopted to obtain the marginal structural map of the images to be repaired. Then, the Criminisi algorithm was used to restore the marginal structure to obtain the complete marginal structure image guided by the superficial linear structure. Finally, the problem of texture inpainting was converted into the counterpart of image denoising through the separation of variables by using the denoising convolutional neural network image denoiser (DnCNN). Compared with the existing inpainting methods, this model has improved the clarity of the marginal structure and reduced the blurring of the area to be repaired.

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Screen Window Propagating for Image Inpainting

Cited by 9 publications

References 19 publications

Enhanced Technique for Exemplar Based Image Inpainting Method

Enhanced Technique for Exemplar Based Image Inpainting Method

An image inpainting method for object removal based on difference degree constraint

A Criminisi-DnCNN Model-Based Image Inpainting Method

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