Surface anomaly detection is a vital component in manufacturing inspection.
Reconstructive anomaly detection methods restore the normal appearance of an
object, ideally modifying only the anomalous regions. Due to the limitations of
commonly used reconstruction architectures, the produced reconstructions are
often poor and either still contain anomalies or lack details in anomaly-free
regions. Recent reconstructive methods adopt diffusion models, however with the
standard diffusion process the problems are not adequately addressed. We
propose a novel transparency-based diffusion process, where the transparency of
anomalous regions is progressively increased, restoring their normal appearance
accurately and maintaining the appearance of anomaly-free regions without loss
of detail. We propose TRANSparency DifFUSION (TransFusion), a discriminative
anomaly detection method that implements the proposed diffusion process,
enabling accurate downstream anomaly detection. TransFusion achieves
state-of-the-art performance on both the VisA and the MVTec AD datasets, with
an image-level AUROC of 98.5% and 99.2%, respectively.

提出了一种新的透明度扩散过程，通过逐渐增加异常区域的透明度，恢复其正常外观并保持不含异常的区域的细节，从而实现精确的异常检测。TransFusion 在 VisA 和 MVTec AD 数据集上获得了 98.5% 和 99.2% 的图像级 AUROC，达到了最先进的性能。