Image Harmonization with Diffusion Model
This addresses image editing challenges for users creating realistic composites, though it appears incremental as it builds on existing diffusion models.
The paper tackles the problem of unrealistic image composites caused by inconsistent lighting between foreground and background images by proposing a diffusion model-based approach for image harmonization, comparing Classifier-Guidance and Classifier-Free methods to adjust illumination and color for seamless blending.
Image composition in image editing involves merging a foreground image with a background image to create a composite. Inconsistent lighting conditions between the foreground and background often result in unrealistic composites. Image harmonization addresses this challenge by adjusting illumination and color to achieve visually appealing and consistent outputs. In this paper, we present a novel approach for image harmonization by leveraging diffusion models. We conduct a comparative analysis of two conditional diffusion models, namely Classifier-Guidance and Classifier-Free. Our focus is on addressing the challenge of adjusting illumination and color in foreground images to create visually appealing outputs that seamlessly blend with the background. Through this research, we establish a solid groundwork for future investigations in the realm of diffusion model-based image harmonization.