EdgeConnect: Structure Guided Image Inpainting using Edge Prediction

Nazeri, Kamyar; Ng, Eric; Joseph, Tony; Qureshi, Faisal Z.; Ebrahimi, Mehran

doi:10.1109/iccvw.2019.00408

“…13(b). Nazeri et al Nazeri et al (2019) are unable to remove the specified region as the existing model was not trained for these shapes.…”

Section: Discussionmentioning

confidence: 99%

“…In this section, the perceptual and statistical results of Crypt-OR are pre- with patch size as and, deep learning schemes of Nazeri et al Nazeri et al (2019) and Yu et al Yu et al (2018). The testing codes corresponding to inpaint model 4 presented by Nazeri et al (2019) and pre-trained adversarial model 5 over Places2 dataset of Yu et al (2018) are considered. The presented results are obtained without fine-tuning as authors have trained with regular and irregular patches provided by Liu et al (2018).…”

Section: Methodsmentioning

confidence: 99%

“…In each figure, the first row represents the original image with imposed redcolored region (Ω), aimed to remove. Second, third, and fourth rows indicate the inpainted images obtained by Criminisi et al (2004), Nazeri et al (2019), Yu et al (2018 and, Crypt-OR respectively. It can easily be observed that Crypt-OR significantly removes the specified region except for minor artifacts in some images.…”

Section: Methodsmentioning

confidence: 99%

“…To connect these edges, researchers have combined theories of the categories mentioned above and proposed hierarchical schemes which improve perceptual and visual plausible Drori et al (2003); Arias et al (2011). Recently, the data-driven and parameter learning-based schemes using Convolutional Neural Networks (CNNs) have been proposed Nazeri et al (2019); Yu et al (2018). Nazeri et al Nazeri et al (2019) scheme is a two-stage approach, i.e., structure prediction and image completion.…”

Section: Image Inpaintingmentioning

confidence: 99%

See 2 more Smart Citations

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Tanwar¹,

Raman²,

Bhargava³

2020

Preprint

0

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<div>Object removal is a technique for removing the undesired object(s) and then fill-in the empty region(s) in an image such that the modified image is visually plausible. The existing algorithms are unable to provide promising results when the region to be removed - has varying textured-neighborhood, is small in size and the depth of the image and, is of specific geometric shapes such as triangle</div><div>and rectangle. In this paper, we proposed a new algorithm by incorporating the merits of partial differential equations (PDEs) and exemplar-based schemes to address these challenges. The data term, which measures the continuity of</div><div>isophotes in exemplar-based methods, is modified by incorporating a regularizer term and partial derivatives up to second order of the input image. This regularizer enhances the strength of isophotes striking the boundary and boosts</div><div>the information propagation in an unbiased manner, in terms of pixel intensity values. Additionally, the low-cost, agility, and accessing flexibility benefits of cloud services have attracted user’s attention today. Besides, users are concerned about utilizing them for their data, as they are supported by untrusted third parties. Addressing these privacy concerns for object-removal in an image over the cloud server, we extended and modified our algorithm to make it compatible for (T; N)-threshold Shamir secret sharing scheme (SSS). This privacy-preserving system is an end-to-end system for object-removal in the ED over the cloud server namely Crypt-OR. Crypt-OR is evaluated by removing synthetically imposed objects in real-images. Further, Crypt-OR has proved to be secure under various pixel-based cryptographic attacks such as frequency-known attack and pixel-correlation attack. </div>

show abstract

“…Recently, the data-driven and parameter learning-based schemes using Convolutional Neural Networks (CNNs) have been proposed Nazeri et al (2019); Yu et al (2018). Nazeri et al Nazeri et al (2019) scheme is a two-stage approach, i.e., structure prediction and image completion. Both of these steps utilize the adversarial model, a variant of deep learning models.…”

Section: Image Inpaintingmentioning

confidence: 99%

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Tanwar¹,

Raman²,

Bhargava³

2020

Preprint

0

View full text Add to dashboard Cite

<div>Object removal is a technique for removing the undesired object(s) and then fill-in the empty region(s) in an image such that the modified image is visually plausible. The existing algorithms are unable to provide promising results when the region to be removed - has varying textured-neighborhood, is small in size and the depth of the image and, is of specific geometric shapes such as triangle</div><div>and rectangle. In this paper, we proposed a new algorithm by incorporating the merits of partial differential equations (PDEs) and exemplar-based schemes to address these challenges. The data term, which measures the continuity of</div><div>isophotes in exemplar-based methods, is modified by incorporating a regularizer term and partial derivatives up to second order of the input image. This regularizer enhances the strength of isophotes striking the boundary and boosts</div><div>the information propagation in an unbiased manner, in terms of pixel intensity values. Additionally, the low-cost, agility, and accessing flexibility benefits of cloud services have attracted user’s attention today. Besides, users are concerned about utilizing them for their data, as they are supported by untrusted third parties. Addressing these privacy concerns for object-removal in an image over the cloud server, we extended and modified our algorithm to make it compatible for (T; N)-threshold Shamir secret sharing scheme (SSS). This privacy-preserving system is an end-to-end system for object-removal in the ED over the cloud server namely Crypt-OR. Crypt-OR is evaluated by removing synthetically imposed objects in real-images. Further, Crypt-OR has proved to be secure under various pixel-based cryptographic attacks such as frequency-known attack and pixel-correlation attack. </div>

show abstract

PKD‐Net: Distillation of prior knowledge for image completion by multi‐level semantic attention

Lu,

Lin,

Xing

et al. 2023

Concurrency and Computation

0

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SummaryPrior knowledge plays a crucial role in image completion. Although almost all of the existing image completion methods use prior knowledge to complete the image to be repaired from different perspectives, the learning and modeling of the prior knowledge is still a challenging problem. In order to address this issue, we propose a novel prior knowledge distillation framework (PKD‐Net) which could distill prior knowledge of structure and style from multiple semantic space and generates not only plausible content but also consistent style with surrounding image area. Our PKD‐Net replaces the skip connection in the vanilla U‐Net with a semantic shift attention module. The semantic shift attention module takes features from encoder layer and those from decoder layer as input pairs to output shifted features which take into account the long‐range dependency of encoder layer features and corresponding decoder layer features from the perspective of local structure and style. Semantic shift attention module models the global interdependencies in local spatial structures (patches centered at each position) and style (appearance texture) dimensions respectively, which could implement distillation of prior knowledge from two aspects: structure and style. Experiments on multiple datasets including faces (CelebA, CelebA‐HQ) and natural images (ImageNet, Places2, Paris Street View) demonstrate that our proposed approach generates higher quality completion results than existing ones.

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EdgeConnect: Structure Guided Image Inpainting using Edge Prediction

Cited by 471 publications

References 39 publications

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

PKD‐Net: Distillation of prior knowledge for image completion by multi‐level semantic attention

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