Enhancing Anomaly Detection in Noisy Images: Unleashing the Power of Attention-Aware PDE Constraint Feature Denoiser Module

Anomaly detection is needed in many applications, such as video surveillance, manufacturing defect detection, and medical image analysis. However, this poses a significant challenge as anomalies can exhibit diverse patterns. Furthermore, the scarcity of labelled anomaly (defective) images complicates the use of supervised classification approaches. To address this, alternative approaches such as unsupervised or self-supervised learning are explored, effectively overcoming challenges posed by limited labelled data. While several techniques exist, there is still significant room for improvement in overall detection results, especially in handling noisy images, which is a realistic dimension often overlooked in existing methods. Therefore, we have proposed a novel framework consisting of various modules, including the Attention-Aware PDE constraint Feature Denoiser Module, Teacher, Student, and Anomaly-Attenuator. Here, we have utilized the loss function in a novel manner, which improves overall performance and ensures consistency. Additionally, we train the network in such a way that a single model would work for multiple types of objects. Finally, we considered different datasets for testing and observed that our model provides superior results, outperforming existing state-of-the-art methods for noisy and clean images.