Abstract Regions with artifacts and semantic inaccuracy are often caused by existing image inpainting algorithms. Moreover, the inpainting effect is limited for images with large missing regions and high-resolution. Therefore, a two-stage image inpainting approach based on parallel adversarial network and multi-condition fusion is proposed in this paper. Firstly, an improved deep residual network is utilized to fill the corrupted image. The first-stage adversarial network is employed to complete the image edge map. Next, the color feature of the filled image is extracted and fused with the completed edge image. Then, the fused image is applied as the condition label of the second-stage adversarial network. Finally, the inpainting result is obtained by the second-stage network with a contextual attention module. Experiments on multiple public datasets demonstrate that realistic inpainting results can be obtained by the proposed approach.
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2020, Vol. 33 
