ESDiff: Encoding Strategy-inspired Diffusion Model with Few-shot Learning for Color Image Inpainting
This addresses the challenge of preserving complex details in image inpainting with limited training data, though it appears incremental as it builds on existing diffusion models.
The paper tackles the problem of color image inpainting by proposing a diffusion model with a novel encoding strategy and few-shot learning, which improves reconstructed image quality in texture and structural integrity, outperforming current techniques in quantitative metrics.
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.