This paper focuses on the quality assessment of the nighttime images, which is technically vulnerable to diverse and sophisticated visual distortions, and proposes an effective noreference image quality assessment method through comprehensive analysis of local and global image attributes. Several quality-aware features are extracted through the study of image brightness, contrast, structure and color. Specifically, the features related to brightness and contrast are extracted through the analysis of local information, while the others are extracted through the analysis of global information. The quality assessment model for night-time images is built by mapping the selected features into subjective ratings via the support vector regression. Extensive experiments conducted on public night-time image database demonstrates the superiority of the proposed method.
Anomaly detection is a challenging task due to the bottleneck of anomalous sample collection. Recently, self-supervised learning has shown great potential for anomaly detection. However, these methods use task-related attributes as selfsupervised signals and are suitable for specific application scenarios. In this paper, we propose a more general selfsupervised method to detect anomaly which regards image quality as a supervised signal. Specially, we convert anomaly detection task into an image quality restoration task which contains distortion and restoration processing. Firstly, we introduce content distortion which erases hardness blocks in normal image based on no reference quality assessment method. Meanwhile, we introduce resolution distortion which down-samples normal images to low-resolution images preserving the essential appearance information. Then, a restoration network is trained to repair such distortions that encourages the model to learn detail information and the content of hardness regions in normal image. Extensive experiments on public benchmark datasets verify the effectiveness and generality of the proposed method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.