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. Pour relever ce défi, 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. Ce
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.

Cet article explore les excursions dans le temps et leurs implications.

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