Latent Feature-Guided Diffusion Models for Shadow Removal

Motivated by the success of diffusionbased Recovering textures under shadows has remained a challenging image restoration models [38, 41], we adapt diffusion problem due to the difficulty of inferring shadowfree models for the task of shadow removal by conditioning on scenes from shadow images. In this paper, we propose the input shadow image and corresponding shadow mask as the use of diffusion models as they offer a promising approach a baseline approach to generate shadow-free images. However, to gradually refine the details of shadow regions preserving and generating high-fidelity textures and during the diffusion process. Our method improves this colors in the shadow region after removal is non-trivial. The process by conditioning on a learned latent feature space baseline model appears to favor borrowing textures from that inherits the characteristics of shadow-free images, thus the surrounding non-shadow areas rather than focusing on avoiding the limitation of conventional methods that condition restoring the original details underneath the shadow, which on degraded images only. Additionally, we propose results in incorrect color mixtures and loss of detail in the to alleviate potential local optima during training by fusing shadow region. In Figure 1, we show one of the representative noise features with the diffusion network. We demonstrate issues of image-mask conditioning, i.e., the model synthesizes the effectiveness of our approach which outperforms results containing an incorrect color mixture.