Feature guidance GAN for high quality image restoration

Ziqiang Pei; Sheng Yang; Guoyou Wang

doi:10.1117/12.2572942

12 June 2020 Feature guidance GAN for high quality image restoration

Ziqiang Pei, Sheng Yang, Guoyou Wang

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115190L (2020) https://doi.org/10.1117/12.2572942
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

Here we propose a novel image inpainting model DFG-GAN, which can effectively alleviate the artifacts problem when the missing region area is too large. Unlike other image inpainting models, our model can transfer the image inpainting task into a GAN task when the mask fills the total image. Apart from that, we also take advantage of the extra class label information to tell what kind of the damaged image is. The more information feed in, the better result shall be. Experiments on several publicly available datasets demonstrate the advantage of the proposed method over existing approaches, regarding both visual fidelity and margin texture.

Citation Download Citation

Ziqiang Pei, Sheng Yang, and Guoyou Wang "Feature guidance GAN for high quality image restoration", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115190L (12 June 2020); https://doi.org/10.1117/12.2572942

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KEYWORDS

Gallium nitride

Image restoration

Computer programming

Image quality

Visualization

Data modeling

Distributed interactive simulations

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