An Image Inpainting Algorithm Based on Energy Minimization

Zhi, Zhanjiang; Sun, Yuwen

doi:10.1142/s0219467812500179

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Most of the traditional face reinforcement algorithms [2,22,23] are based on the relative position structure of face , so it is difficult to complete the face under different rotation or tilt positions. Because of the limitations of planar face structure inpainting and the development of artificial intelligence, Xiong et al, Zhi and Sun, Nazeri et al, and Liao and Yan [24][25][26][27] proposed the face image inpainting method based on convolutional neural network and Liu et al, Li et al, Liao et al, and Portenier et al [28][29][30][31] proposed the face image inpainting method based on generative adversarial nets. Meanwhile, the emergence of 3D face image inpainting [32] based on deep learning made face image inpainting achieve better effect.…”

Section: Face Inpaintingmentioning

confidence: 99%

3D Face Image Inpainting with Generative Adversarial Nets

Wei

Li²,

Liu³

et al. 2020

Mathematical Problems in Engineering

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In the process of face recognition, face acquisition data is seriously distorted. Many face images collected are blurred or even missing. Faced with so many problems, the traditional image inpainting was based on structure, while the current popular image inpainting method is based on deep convolutional neural network and generative adversarial nets. In this paper, we propose a 3D face image inpainting method based on generative adversarial nets. We identify two parallels of the vector to locate the planer positions. Compared with the previous, the edge information of the missing image is detected, and the edge fuzzy inpainting can achieve better visual match effect. We make the face recognition performance dramatically boost.

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Section: Face Inpaintingmentioning

confidence: 99%

3D Face Image Inpainting with Generative Adversarial Nets

Wei

Li²,

Liu³

et al. 2020

Mathematical Problems in Engineering

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An Integrated Repainting System for Digital Restoration of Vijayanagara Murals

Aswatha

Mukherjee

Bhowmick

2016

Int. J. Image Grap.

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An integrated repainting system is proposed in this paper for digital restoration of images of heritage murals, which have historical significance in their painting styles and ritualistic contents. The repainting system uses an ensemble of conventional image processing tools, in tandem with some state-of-the-art image rendition techniques, such as scaled bilateral filtering, source-constrained inpainting, tonal processing, and texture mapping based on gradient fusion. Murals that are old by nearly four centuries, have been imaged in situ from the walls of temples under a controlled environment, and then they have been fed to our repainting system. As the work of mural art is highly subjective, and so is its interpretation, a battery of tests for subjective evaluation has been performed to compare the different stages of restoration. Three different tournament strategies have been followed to make the test result devoid of any subjective bias as far as possible. The overall evaluation result is quite encouraging, as the restored images exhibit a gradually improving quality through the different stages of restoration.

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Video Inpainting: A Complete Framework

Zarif

Ibrahim

2021

Int. J. Image Grap.

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Reconstructing and repairing of corrupted or missing parts after object removal in digital video is an important trend in artwork restoration. Video inpainting is an active subject in video processing, which deals with the recovery of the corrupted or missing data. Most previous video inpainting approaches consume more time in extensive search to find the best patch to restore the damaged frames. In addition to that, most of them cannot handle the gradual and sudden illumination changes, dynamic background, full object occlusion, and object changes in scale. In this paper, we present a complete video inpainting framework without the extensive search process. The proposed framework consists of a segmentation stage based on low-resolution version and background subtraction. A background inpainting stage is applied to restore the damaged background regions after static or moving object removal based on the gray-level co-occurrence matrix (GLCM). A foreground inpainting stage is based on objects repository. GLCM is used to complete the moving occluded objects during the occlusion. The proposed method reduces the inpainting time from hours to a few seconds and maintains the spatial and temporal consistency. It works well when the background has clutter or fake motion, and it can handle the changes in object size and in posture. Moreover, it is able to handle full occlusion and illumination changes.

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An Image Inpainting Algorithm Based on Energy Minimization

Cited by 3 publications

References 12 publications

3D Face Image Inpainting with Generative Adversarial Nets

3D Face Image Inpainting with Generative Adversarial Nets

An Integrated Repainting System for Digital Restoration of Vijayanagara Murals

Video Inpainting: A Complete Framework

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