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This paper presents the development of two innovative image inpainting models: Spatially-Enhanced Generative Adversarial Network (SE GAN) and Dual-Stream Enhanced Generative Adversarial Network (DSE GAN). These models are designed to enable missing or damaged digital images to be filled in, with appropriate context, thus improving digital image restoration. However, SE GAN improves spatial resolution in the generative framework while DSE GAN concurrently integrates structural and textural information. Both models are trained on various datasets, and they achieve performance on aesthetic quality and structural integrity better than current techniques. SE GAN and DSE GAN represent important advancement in the field of digital image processing, and potentially have applications in video editing, interactive media, and artistic restoration. Through deep learning techniques, this work creates space for more effective and creative solutions in digital media restoration, upgrading the generative models and establishing new benchmarks for high-fidelity restoration of content.

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Enhanced Image Inpainting Using Spatially-Aware and Dual-Stream Generative Adversarial Networks for High-Fidelity Content Restoration

  • 会议论文

  • 发表日期：

    2026年

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  • E. Andrew Sam,
  • S. Bhuvanesh,
  • R. Madhumitha

摘要

This paper presents the development of two innovative image inpainting models: Spatially-Enhanced Generative Adversarial Network (SE GAN) and Dual-Stream Enhanced Generative Adversarial Network (DSE GAN). These models are designed to enable missing or damaged digital images to be filled in, with appropriate context, thus improving digital image restoration. However, SE GAN improves spatial resolution in the generative framework while DSE GAN concurrently integrates structural and textural information. Both models are trained on various datasets, and they achieve performance on aesthetic quality and structural integrity better than current techniques. SE GAN and DSE GAN represent important advancement in the field of digital image processing, and potentially have applications in video editing, interactive media, and artistic restoration. Through deep learning techniques, this work creates space for more effective and creative solutions in digital media restoration, upgrading the generative models and establishing new benchmarks for high-fidelity restoration of content.