Image inpainting based on adaptive generative models

N. Gapon; Artyem Puzerenko; V. Voronin; M. Zhdanova; E. Semenishchev

doi:10.1117/12.3017675

7 June 2024 Image inpainting based on adaptive generative models

N. Gapon, Artyem Puzerenko, V. Voronin, M. Zhdanova, E. Semenishchev

Proceedings Volume 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI; 130511S (2024) https://doi.org/10.1117/12.3017675
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

This article addresses the challenge of image restoration using generative adversarial networks (GANs) specifically tailored for images that include undesired objects. This task becomes crucial in scenarios where there is a need to eliminate incidental elements like random pedestrians or obstructive objects such as text, symbols, or drawings that hinder the main content's clarity. These objects can sometimes entirely obscure critical parts of the image, leading to a distortion of the intended information. In this study, we introduce an innovative approach to image reconstruction by leveraging a generative adversarial network (GAN) architecture enhanced with a two-path discriminator for distinct texture and color analysis. Our model, which integrates the stability advantages of Wasserstein GANs, effectively addresses common GAN challenges like mode collapse and training instability. The sophisticated design of our generator and discriminator results in superior image quality for reconstruction tasks, surpassing the performance of traditional single-path GANs in terms of accuracy and visual fidelity.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

N. Gapon, Artyem Puzerenko, V. Voronin, M. Zhdanova, and E. Semenishchev "Image inpainting based on adaptive generative models", Proc. SPIE 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, 130511S (7 June 2024); https://doi.org/10.1117/12.3017675

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