Abstract
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.
Abstract (translated)
图像修复是指重建图像中丢失或损坏的部分，使其与周围内容无缝融合。随着先进生成模型（尤其是扩散模型和生成对抗网络）的出现，图像修复在视觉质量和连贯性方面取得了显著的进步。然而，实现无缝连续性仍然是一个重大挑战。在这项工作中，我们提出了两种新颖的方法来解决基于扩散模型的图像修复中的不一致问题。首先，我们引入了一种改进的变分自编码器，可以纠正颜色失衡，确保最终修复的结果没有颜色差异。其次，我们提出了一种两步训练策略，通过这种方法可以在扩散过程中改善生成内容与现有图像内容之间的融合效果。通过广泛的实验，我们证明了我们的方法能够有效减少不连续性，并产生高质量、连贯且视觉上吸引人的图像修复结果。
URL
https://arxiv.org/abs/2506.12530
