2019
DOI: 10.1049/iet-cvi.2018.5623
|View full text |Cite
|
Sign up to set email alerts
|

Image unmosaicing without location information using stacked GAN

Abstract: Image mosaicing is an image processing technique that is most commonly used to conceal identities of sensitive objects. The authors' research features recovering the mosaiced parts in an image, especially focusing on facial parts. While recent image completion methods based on deep learning have shown promising results on recovering damaged parts in an image, they have not addressed the problem of image unmosaicing. Moreover, all those methods necessitate the location information of damaged parts to tackle the… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

0
6
0

Year Published

2019
2019
2023
2023

Publication Types

Select...
7

Relationship

5
2

Authors

Journals

citations
Cited by 8 publications
(6 citation statements)
references
References 36 publications
0
6
0
Order By: Relevance
“…Structural inpainting can inpaint the complex structures. Khan et al [13] proposed two stage GAN to de-pixelate the mosaic face image. Their network first removes the mosaic part in the image and then generates face semantics.…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…Structural inpainting can inpaint the complex structures. Khan et al [13] proposed two stage GAN to de-pixelate the mosaic face image. Their network first removes the mosaic part in the image and then generates face semantics.…”
Section: Related Workmentioning
confidence: 99%
“…Refs. [13,35] are limited to square-shape corrupted areas only, but shadows can be an irregular shape. Similar to [7,8,24], we exploit both low-level (l 1 ) loss and high-level (SSIM) loss in terms of reconstruction loss to inpaint the region under the shadow.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…They match patches in feature space, which are finally translated to a high-resolution output image. Recently, a two-stage network to inpaint the mosaiced area in a face image was proposed in [14]. It removes the mosaic and generated face semantics in a coarse-to-fine manner.…”
Section: Related Workmentioning
confidence: 99%
“…The proposed model is trained for two loss functions, binary cross entropy (BCE) and structural similarity index measure (SSIM). The BCE loss function tries to maximize the difference of the probability distribution between two classes, in this case, lesion and nonlesion voxels [ 7 ]. SSIM, on the other hand, is a perception-based loss function that quantifies the similarity between two images [ 8 ].…”
Section: Introductionmentioning
confidence: 99%