Trans-Adapter：用于透明图像修复的即插即用框架
Trans-Adapter: A Plug-and-Play Framework for Transparent Image Inpainting
AI总结：
针对现有修复方法无法直接处理RGBA透明图像且传统两阶段流程存在透明度不一致和边缘锯齿的问题，提出即插即用适配器Trans-Adapter，使扩散修复模型直接处理透明图像，并引入LayerBench基准与无参考边缘质量指标验证有效性。
摘要：
RGBA图像凭借额外的alpha通道，对于任何需要混合、遮罩或透明效果的应用都至关重要，使其比标准RGB图像更具通用性。然而，现有的图像修复方法仅针对RGB图像设计。传统的透明图像修复方法通常涉及在RGBA图像下方放置背景，并采用两阶段流程：先进行图像修复，再进行图像抠图。然而，该流程难以在编辑区域保持透明度一致性，且抠图可能会在透明度边界处引入锯齿状边缘。为解决这些挑战，我们提出了Trans-Adapter，一种即插即用的适配器，使基于扩散的修复模型能够直接处理透明图像。Trans-Adapter还通过ControlNet支持可控编辑，并可无缝集成到各种社区模型中。为评估我们的方法，我们引入了LayerBench，以及一种新颖的无参考alpha边缘质量评估指标，用于评估透明度边缘质量。我们在LayerBench上进行了大量实验，以证明我们方法的有效性。
英文摘要：
RGBA images, with the additional alpha channel, are crucial for any application that needs blending, masking, or transparency effects, making them more versatile than standard RGB images. Nevertheless, existing image inpainting methods are designed exclusively for RGB images. Conventional approaches to transparent image inpainting typically involve placing a background underneath RGBA images and employing a two-stage process: image inpainting followed by image matting. This pipeline, however, struggles to preserve transparency consistency in edited regions, and matting can introduce jagged edges along transparency boundaries. To address these challenges, we propose Trans-Adapter, a plug-and-play adapter that enables diffusion-based inpainting models to process transparent images directly. Trans-Adapter also supports controllable editing via ControlNet and can be seamlessly integrated into various community models. To evaluate our method, we introduce LayerBench, along with a novel non-reference alpha edge quality evaluation metric for assessing transparency edge quality. We conduct extensive experiments on LayerBench to demonstrate the effectiveness of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
