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Generative Image Inpainting Based on Wavelet Transform Attention Model | IEEE Conference Publication | IEEE Xplore

Generative Image Inpainting Based on Wavelet Transform Attention Model


Abstract:

Image inpainting is a challenging task in image processing and widely applied in many areas such as photo editing. Traditional patch-based methods are not effective to de...Show More

Abstract:

Image inpainting is a challenging task in image processing and widely applied in many areas such as photo editing. Traditional patch-based methods are not effective to deal with complex or non-repetitive structures. Recently, deep learning-based approaches have shown promising results for image inpainting. However, they usually generate contents with artificial boundaries, distorted structures or blurry textures. To handle this problem, we propose a novel image inpainting method based on wavelet transform attention model (WTAM). The wavelet transform decomposes features into multi-frequency sub-bands for extracting and transmitting deep information, and the attention mechanism enhances the ability of wavelet transform to capture significant detailed information in each level's subband images. Extensive experimental results on multiple datasets (Paris StreetView, CelebA and CelebAMask-HQ) demonstrate that our method can not only synthesize sharp image structures but also generate fine-detailed textures in missing regions, significantly outperforming the state-of-the-art methods.
Date of Conference: 12-14 October 2020
Date Added to IEEE Xplore: 28 September 2020
Print ISBN:978-1-7281-3320-1
Print ISSN: 2158-1525
Conference Location: Seville, Spain

References

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