Segment Anything (SAM), an advanced universal image segmentation model trained on an expansive visual dataset, has set a new benchmark in image segmentation and computer vision. However, it faced challenges when it came to distinguishing between shadows and their backgrounds. To address this, we developed Deshadow-Anything, considering the generalization of large-scale datasets, and we performed Fine-tuning on large-scale datasets to achieve image shadow removal. The diffusion model can diffuse along the edges and textures of an image, helping to remove shadows while preserving the details of the image. Furthermore, we design Multi-Self-Attention Guidance (MSAG) and adaptive input perturbation (DDPM-AIP) to accelerate the iterative training speed of diffusion. Experiments on shadow removal tasks demonstrate that these methods can effectively improve image restoration performance.

翻译：分割一切（SAM）作为一种基于大规模视觉数据集训练的高级通用图像分割模型，已在图像分割与计算机视觉领域树立了新标杆。然而，该模型在区分阴影与背景时仍面临挑战。为解决此问题，我们开发了Deshadow-Anything方法，充分考虑大规模数据集的泛化能力，并对大规模数据集进行微调以实现图像阴影去除。扩散模型可沿图像边缘与纹理进行扩散，有助于在保留图像细节的同时去除阴影。此外，我们设计了多自注意力引导（MSAG）与自适应输入扰动（DDPM-AIP）以加速扩散模型的迭代训练速度。在阴影去除任务上的实验表明，这些方法能有效提升图像恢复性能。