Image inpainting with gradient guidance

Min Wang; Yucheng Fu; Zhu Lin; Li Guo; Rong Chu; Dahai Jing

doi:10.1117/12.2604562

5 October 2021 Image inpainting with gradient guidance

Min Wang, Yucheng Fu, Zhu Lin, Li Guo, Rong Chu, Dahai Jing

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119110G (2021) https://doi.org/10.1117/12.2604562
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

To generate images with complete structure and clear content, a gradient-guided image inpainting method is proposed by introducing the gradient branch to guide the image inpainting. At the same time, in order to better fuse the branch features of gradient map and repair results of generator network, a feature equalization module with attention mechanism is introduced, to effectively balance features and inhibit learning unimportant feature information. Finally, in order to avoid using KL divergence and JS divergence to measure the distribution gap between two samples, this paper uses Wasserstein distance to measure the sample gap, and designs the adversarial-discriminative network based on WGANGP. Experiments on Paris StreetView and CelebA datasets show that our method can obtain satisfactory repair results with complete structure and clear content.

Citation Download Citation

Min Wang, Yucheng Fu, Zhu Lin, Li Guo, Rong Chu, and Dahai Jing "Image inpainting with gradient guidance", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119110G (5 October 2021); https://doi.org/10.1117/12.2604562

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KEYWORDS

Image enhancement

Convolution

Image restoration

Image processing

Feature extraction

Computer programming

Digital image processing

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