# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 1 Introduction  
In the field of computer vision, visual anomaly detection is a crucial issue that involves the process of detecting and pinpointing data instances that deviate from the expected or standard observations. In the past years, Anomaly detection had a wide range of applications in fields such as industrial quality inspection [1; 2; 3], medical diagnosis [4], and video surveillance [5; 6]. In industrial production processes, product quality is highly susceptible to various factors, such as existing technologies and working conditions. Surface defects are the most visible manifestation of the impact on product quality. Therefore, surface defect detection is crucial to ensure product qualification and reliability. Anomaly detection plays a vital role in industrial quality inspection by identifying and locating defects in product appearance, thereby improving product quality and ensuring compliance with standards. In recent years, with the rapid development of the computer vision field, deep learning-based methods have become an effective solution for industrial quality inspection. However, due to the limited number of anomaly samples and the difficulty of including all types of anomalies in a limited number of samples, and the labor-intensive data labeling process, supervised anomaly detection methods are severely limited. As a result, most recent research has focused on unsupervised learning methods, which aim to achieve anomaly detection without requiring prior information on anomaly samples. Unsupervised learning-based anomaly detection has become a promising research trend for the future. In unsupervised anomaly detection methods, there are mainly two types of approaches: feature embedding-based and reconstruction-based methods. Feature embedding-based methods [7; 8; 9; 10; 11] that utilize pre-trained models to extract image features and realize the measurement or comparison of features by feature modeling, have been widely used inthe field of unsupervised anomaly detection. However, feature embedding-based methods rely on pre-trained models on additional datasets such as ImageNet, and their performance may significantly decrease when the features of industrial images differ significantly from those of the pre-training dataset. Additionally, feature embedding-based methods are highly dependent on the consistency of the placement of the target and the shooting angle. Otherwise, false detections are likely to occur. In contrast, reconstruction-based methods [12; 13; 14; 15] do not have this limitation and do not require additional training data, making them suitable for various scenarios. Reconstruction-based methods exhibit better anomaly detection and localization performances for randomly placed objects. However, reconstruction-based methods also have limitations. They may not be able to achieve complete reconstruction when dealing with images with complex structures and textures. In addition, reconstruction-based methods may result in invariant reconstruction of anomalous regions due to their strong reconstruction ability. To address the aforementioned issues, we propose a reconstruction-based method using the noise-to-norm paradigm, which trains the reconstruction network on noisy images as input, significantly improving the network's reconstruction ability. Additionally, the introduction of noise disturbs the anomalous regions, making it difficult to distinguish them from normal patterns, thus solving the problem of invariant reconstruction of anomalous regions. Our proposed reconstruction model is based on M-net [16] and employs a multiscale fusion structure. Before being fed into the reconstruction network, the noisy image is downsampled to varied sizes to enlarge the model's receptive field, providing better robustness to anomalous regions of diverse sizes. The reconstruction network comprises three parts: an encoder, a decoder, and a feature fusion module; both the encoder and decoder contain residual attention modules and skip connections between them.