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  4. Vol 9, Issue 1, 2025

PGGAN: Probability Guided Generative Adversarial Network for Image Inpainting

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DOI: 10.23977/acss.2025.090106 | Downloads: 23 | Views: 576

Author(s)

Chaoqun Dong 1, Yang Yang 1

Affiliation(s)

1 School of Information Science and Technology, Yunnan Normal University, Kunming, China

Corresponding Author

Yang Yang

ABSTRACT

Probability Guided Generative Adversarial Network (PG-GAN) aims to address key challenges in image inpainting, particularly in capturing structural information over long distances. Firstly, we design the IAModule, which provides semantic attention based on the distribution characteristics of input features, thereby enhancing semantic coherence in image inpainting. Secondly, we propose RR-SSIM Loss, a new loss function aimed at solving the problem of Structural Similarity (SSIM) that is difficult to capture long-distance structural information through sliding window calculations. Finally, we provide a new feature enhancement mechanism through channel dimension Fourier transform and design it as a HybridFFTModule. This module enhances the distinguishability of global representation through channel modeling, effectively adjusting the representation space of global information and further improving the effectiveness of image inpainting. In the experimental section, we validate the superior performance of PG-GAN on CelabA-HQ dataset. In summary, our PG-GAN provides a new and effective method for image inpainting, with broad application prospects.

KEYWORDS

Image Inpainting, Generative Adversarial Network (GAN), Deep Learning, Fourier Transform

CITE THIS PAPER

Chaoqun Dong, Yang Yang, PGGAN: Probability Guided Generative Adversarial Network for Image Inpainting. Advances in Computer, Signals and Systems (2025) Vol. 9: 33-41. DOI: http://dx.doi.org/10.23977/acss.2025.090106.

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