Abstract
Image inpainting is a technique used to restore missing or damaged regions of an image. Traditional methods primarily utilize information from adjacent pixels for reconstructing missing areas, while they struggle to preserve complex details and structures. Simultaneously, models based on deep learning necessitate substantial amounts of training data. To address this challenge, an encoding strategy-inspired diffusion model with few-shot learning for color image inpainting is proposed in this paper. The main idea of this novel encoding strategy is the deployment of a "virtual mask" to construct high-dimensional objects through mutual perturbations between channels. This approach enables the diffusion model to capture diverse image representations and detailed features from limited training samples. Moreover, the encoding strategy leverages redundancy between channels, integrates with low-rank methods during iterative inpainting, and incorporates the diffusion model to achieve accurate information output. Experimental results indicate that our method exceeds current techniques in quantitative metrics, and the reconstructed images quality has been improved in aspects of texture and structural integrity, leading to more precise and coherent results.
Abstract (translated)
图像修复是一种用于恢复图片中缺失或损坏区域的技术。传统方法主要依赖于相邻像素的信息来重建丢失的区域，但这些方法在保留复杂细节和结构方面存在困难。同时，基于深度学习的方法需要大量的训练数据。为了应对这一挑战，本文提出了一种受编码策略启发的、适用于彩色图像修复的少量样本学习扩散模型。 这种新型编码策略的核心思想是使用“虚拟掩模”通过通道之间的相互扰动构建高维对象。这种方法使扩散模型能够从有限的训练样本中捕捉到多样的图像表示和细节特征。此外，该编码策略利用了通道间的冗余性，并在迭代修复过程中与低秩方法相结合，同时整合了扩散模型以实现精确的信息输出。 实验结果表明，我们的方法在定量指标上超越了当前技术，在纹理质量和结构完整性方面提升了重建图像的质量，从而实现了更为精准和连贯的结果。
URL
https://arxiv.org/abs/2504.17524
