Abstract
This paper proposes an end-to-end Learnable Edge-Attention Map (LEAM) method to assist image inpainting. To achieve a better-recovered effect, we design an edge attention module, which extracts the feature information of the edge map and re-normalizes the image feature information when automatically updating the edge map. And the information of known regions is adopted to assist the decoder generates semantically consistent results. A dual-discriminator structure consisting of the local discriminator and global discriminator is proposed to generate realistic texture details and improve the consistency of the overall structure. Experiments show that our method can obtain higher image inpainting quality than the existing state-of-the-art approaches, which improves PSNR by 3.58%, SSIM by 2.27%, and reduce MAE by 9.21% on average.
Highlights
Image inpainting aims at reconstructing missing regions of images according to the known content [1]
The main challenge of image inpainting is to generate realistic texture details in the missing areas and maintain the semantic structure of global images [4], which can effectively affect the visual quality of images
We propose a learnable edge-attention map method, which aims to utilize feature information for generating credible content effectively
Summary
Image inpainting aims at reconstructing missing regions of images according to the known content [1]. Some methods with spatial attention [9], [10] use the surrounding image features to recover the missing area These methods can ensure the semantic consistency of the generated content, but they only focus on rectangular holes. We propose a learnable edge-attention map method, which aims to utilize feature information for generating credible content effectively. More feature information can be retained to the deep network layers by the attention module, providing possible preconditions for the reasonable structure information generation. The single discriminator of this method could not sufficiently handle the irregular holes, especially for large areas missing Based on these insights, U-Net [12] is used as the backbone of our generator to retain sufficient feature information of each layer. This paper is organized as follows: in Section II, we give the related work of image inpainting; Section III describes the proposed method details; Section IV shows the experimental results and analysis; Section V summarizes the paper and prospects the future work
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