解决图像修复与扩展中的衔接不自然问题。
Towards Seamless Borders: A Method for Mitigating Inconsistencies in Image Inpainting and Outpainting
- 用改进的变分自编码器修正颜色偏差。
- 采用两阶段训练提升生成内容与原图融合度。
- 适合需要高质量图像修补的视觉应用。
图像修复旨在以无缝方式重建图像中缺失或损坏的部分。随着扩散模型和生成对抗网络等先进生成模型的发展，图像修复在视觉质量和连贯性方面取得了显著进步。然而，实现完全无缝的连续性仍是重大挑战。本文提出两种新方法，用于缓解基于扩散模型的图像修复中的不一致性问题。首先，引入一种改进的变分自编码器，以校正颜色失衡，确保最终修复结果无颜色错配。其次，提出一种两步训练策略，优化扩散过程中生成内容与原始图像之间的融合效果。通过大量实验验证，所提方法能有效降低不连续性，生成在视觉上连贯且高质量的修复结果。
原文摘要 · Abstract (English)
Image inpainting is the task of reconstructing missing or damaged parts of an image in a way that seamlessly blends with the surrounding content. With the advent of advanced generative models, especially diffusion models and generative adversarial networks, inpainting has achieved remarkable improvements in visual quality and coherence. However, achieving seamless continuity remains a significant challenge. In this work, we propose two novel methods to address discrepancy issues in diffusion-based inpainting models. First, we introduce a modified Variational Autoencoder that corrects color imbalances, ensuring that the final inpainted results are free of color mismatches. Second, we propose a two-step training strategy that improves the blending of generated and existing image content during the diffusion process. Through extensive experiments, we demonstrate that our methods effectively reduce discontinuity and produce high-quality inpainting results that are coherent and visually appealing.
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